diff --git a/README.md b/README.md
index 184235e..7931275 100644
--- a/README.md
+++ b/README.md
@@ -25,6 +25,7 @@ Recipes and reference apps for the [Tango API](https://tango.makegov.com), prima
 | Example | What it shows |
 | --- | --- |
 | [`opportunities-agent`](./examples/opportunities-agent/) | A minimal Claude tool-use loop that answers plain-English questions about federal opportunities. |
+| [`incumbent-on-post`](./examples/incumbent-on-post/) | A new opportunity posts → brief the likely incumbent, prior award, and funding. A PydanticAI agent over the Tango MCP (the repo's first MCP integration), hardened with a deterministic scorer + evals. |
 | [`saved-search-watcher`](./examples/saved-search-watcher/) | A YAML-driven watcher: poll a saved search on a schedule, diff against a JSON state file, alert on what's new. |
 | [`webhook-receiver`](./examples/webhook-receiver/) | A FastAPI app that accepts Tango webhook deliveries — signature-verified, idempotent, with a pluggable sink. |
 
diff --git a/examples/README.md b/examples/README.md
index 997a49c..ba3721c 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -5,6 +5,7 @@ More ambitious patterns built on the Tango API. Where [`notebooks/`](../notebook
 | Example | What it shows |
 | --- | --- |
 | [`opportunities-agent/`](./opportunities-agent/) | A minimal Claude tool-use loop that lets you ask plain-English questions about federal opportunities. |
+| [`incumbent-on-post/`](./incumbent-on-post/) | When a new opportunity posts, brief the likely incumbent, prior award, and funding — a PydanticAI agent over the Tango MCP, plus a deterministic scorer and evals. The repo's first MCP integration. |
 | [`saved-search-watcher/`](./saved-search-watcher/) | A YAML-driven watcher: pull a saved search on a schedule, diff against a JSON state file, alert on what's new. |
 | [`tango-lookup-extension/`](./tango-lookup-extension/) | A Chrome extension (Manifest V3, vanilla JS) for quick lookups of entities, contracts, IDVs, and opportunities from the toolbar. |
 | [`webhook-receiver/`](./webhook-receiver/) | A FastAPI app that accepts Tango webhook deliveries — signature-verified, idempotent, with a pluggable sink. |
diff --git a/examples/incumbent-on-post/README.md b/examples/incumbent-on-post/README.md
new file mode 100644
index 0000000..f4eadcf
--- /dev/null
+++ b/examples/incumbent-on-post/README.md
@@ -0,0 +1,77 @@
+# Incumbent-on-post
+
+A new opportunity hits SAM.gov. Before you've finished reading the title, you want three things: **who's the incumbent, what was the prior award worth, and is the money still flowing?** This example answers all three from one notice — and is honest about the one question that has no clean answer.
+
+It's also the cookbook's **first MCP integration**. Every other example hand-wires the `tango-python` SDK; here the tool catalog comes from the hosted [Tango MCP server](https://govcon.dev/mcp), and a [PydanticAI](https://ai.pydantic.dev) agent drives it.
+
+![incumbent-on-post pipeline: one notice pivots on the contracting office, runs through a deterministic scorer and a PydanticAI agent over the Tango MCP, and resolves into a ranked IncumbentBrief](./demo.svg)
+
+## The join you can't make
+
+The obvious idea — match the new opportunity to the prior contract on the solicitation number — **does not work**. A fresh recompete gets a brand-new solicitation number; the predecessor award carries the old one. They never match. There is no clean key from notice → incumbent.
+
+So you pivot on the thing that *persists* across a recompete: the **contracting office**. The incumbent is whoever holds an award out of that office, in that NAICS/PSC, **whose clock is running out** — ranked by period-of-performance / IDV `last_date_to_order` landing near the notice, not by who won most recently (recency surfaces brand-new awards, the opposite of a recompete). And because that's an inference, the honest output is a **ranked shortlist with confidence and evidence**, never a single asserted name — plus a first-class *"likely new work / no clear predecessor"* answer for first-time buys and in-house work.
+
+## The arc: plausible → shippable
+
+The example is two files on purpose.
+
+**1. `brief.py` — the agent (the easy version).** A PydanticAI agent pointed at the Tango MCP. It resolves the office, searches contracts and IDVs, and returns a typed `IncumbentBrief`. The MCP wiring is the whole point — it's about ten lines:
+
+```python
+tango_mcp = MCPServerStreamableHTTP("https://govcon.dev/mcp", headers={"X-Tango-API-Key": api_key})
+agent = Agent("anthropic:claude-sonnet-4-6", toolsets=[tango_mcp], output_type=IncumbentBrief, instructions=DOCTRINE)
+async with agent:
+    result = await agent.run(notice_prompt)
+```
+
+It gets a *plausible* answer. Plausible isn't shippable: the ranking is improvised fresh each run, so you can't tell when it regresses.
+
+**2. `scorer.py` — the deterministic scorer (the honest version).** The same office-pivot, but the ranking is a plain scored function — recompete-window proximity, dollar magnitude, vendor recurrence — bounded server-side with the SDK's `expiring_gte/lte` and `last_date_to_order_gte/lte` filters. No model in the ranking, so it's reproducible and **eval-able** (`evals.py` pins it to invariants). Run this first; fall through to the agent's scope-match judgment only when it comes back thin.
+
+That's the lesson the recipe is really teaching: **let the model do the fuzzy language step (does this award's scope match the notice?), and keep the ranking deterministic.**
+
+## Run it
+
+From the repo root:
+
+```bash
+just incumbent                 # runs the agent on examples/incumbent-on-post/sample_notice.json
+just incumbent path/to/notice.json
+just incumbent-evals           # deterministic scorer invariants — no LLM key needed
+```
+
+`just incumbent` needs `TANGO_API_KEY` (for the MCP server) **and** a model — by default `ANTHROPIC_API_KEY` for the hosted Anthropic model, or a local server (see below). `just incumbent-evals` needs only `TANGO_API_KEY`.
+
+### The notice shape
+
+The agent and scorer take an enriched notice — what a Tango opportunities webhook gives you, plus a `get_opportunity` lookup to fill in office/NAICS/PSC. See [`sample_notice.json`](./sample_notice.json). The field that matters most is `office_code`: pass the **most specific** awarding office, not the parent department. Department-level filtering (`VA`, `DHS`) returns noise — DHS alone has 22 components.
+
+## Running locally
+
+There are two independent "local" axes here — the **MCP server** (where the Tango tools live) and the **model** (the LLM driving them). You can move either off the default without touching the other.
+
+**Local model.** By default the agent uses the hosted Anthropic model. To run it against any OpenAI-compatible server (LM Studio, Ollama, vLLM, …), set `OPENAI_BASE_URL` and `MODEL`; `ANTHROPIC_API_KEY` is then no longer required:
+
+```bash
+OPENAI_BASE_URL=http://localhost:1234/v1 MODEL=your-served-model-id just incumbent
+```
+
+Pick a model that does real tool-calling — this agent lives or dies on driving the Tango MCP tools, so a model that can't reliably emit tool calls will just return an empty brief.
+
+**Local MCP server.** By default `brief.py` talks to the hosted server at `https://govcon.dev/mcp` with your key in the `X-Tango-API-Key` header. To run the [Tango MCP server](https://tango.makegov.com) locally over stdio instead, swap `MCPServerStreamableHTTP` for `MCPServerStdio`, or point `TANGO_MCP_URL` at your own deployment.
+
+## Wiring it to a real trigger
+
+This example takes a notice on the command line. To make it a pipeline, feed it from the [`webhook-receiver`](../webhook-receiver/) (low-latency, server-side) or the [`saved-search-watcher`](../saved-search-watcher/) (you own the cadence): on each *new* opportunity, run the scorer, fall through to the agent when thin, and push the brief. Cap the fan-out — a big webhook batch shouldn't trigger dozens of entity/budget lookups.
+
+## Where to take it next
+
+- **Funding signal.** The `IncumbentBrief.funding` field is wired through the schema and prompt but is the lightest part of the agent's job. Deepen it: pull `get_budget_account` + quarterly cash flow for the office's account and report the enacted→obligated YoY delta as a real "is the money there" read. Gate it to high-value opportunities so you stay under your lookup budget.
+- **Scope match as a model step.** The scorer ranks on expiry + magnitude + recurrence; it does *not* read the PWS. Add an opt-in step where the model scores scope similarity between the notice description and each candidate's award description — that's the one judgment code can't make.
+- **Grow the eval set.** `evals.py` ships three structural cases. Every time the scorer surprises you, add the case. That's how you learn whether the office→category join is good enough before you trust it.
+
+## Caveats
+
+- **Not run in CI.** `brief.py` calls a paid LLM and a live API and is non-deterministic. `evals.py` is deterministic but still hits the live API, so it isn't part of the notebook CI either. Treat both as starting points.
+- **The incumbent is always an inference.** Office realignments, bridge/sole-source extensions, work moving between vehicles, and genuinely new buys all break the office pivot. The shortlist is a lead, not a finding — which is exactly why the schema won't let it assert a single name.
diff --git a/examples/incumbent-on-post/brief.py b/examples/incumbent-on-post/brief.py
new file mode 100644
index 0000000..d614717
--- /dev/null
+++ b/examples/incumbent-on-post/brief.py
@@ -0,0 +1,212 @@
+"""
+Incumbent-on-post — the agent.
+
+A new opportunity hits SAM.gov. This script takes that notice, points a PydanticAI
+agent at the *hosted Tango MCP server*, and asks: who's the likely incumbent, what
+was the prior award worth, and is the money still flowing? The model drives the
+Tango tools (resolve / search / search_opportunities / get_details) itself and
+returns a typed `IncumbentBrief`.
+
+This is the cookbook's first MCP integration. Everything else in examples/ hand-wires
+the tango-python SDK; here the tool catalog comes from the MCP server, so the only
+thing this file owns is the *doctrine* (the office-pivot prompt) and the *output
+contract* (the schema below).
+
+This is the "easy version." It gets a plausible answer. Plausible isn't shippable —
+see scorer.py for the deterministic ranking that makes the incumbent join reproducible
+and eval-able, and the README for why you want both.
+
+Run:
+    just incumbent                       # uses sample_notice.json
+    just incumbent path/to/notice.json
+
+Requires TANGO_API_KEY (for the MCP server) and a model:
+  - default: ANTHROPIC_API_KEY + the hosted Anthropic model.
+  - local / OpenAI-compatible: set OPENAI_BASE_URL (e.g. http://localhost:1234/v1 for
+    LM Studio or Ollama) and MODEL to the served model id. Pick a model that does
+    real tool-calling — this agent lives or dies on driving the Tango MCP tools.
+
+Not run in CI: it calls a paid LLM and a live API, and the output is non-deterministic.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import os
+import sys
+from pathlib import Path
+from typing import Literal
+
+from pydantic import BaseModel, Field
+from pydantic_ai import Agent
+from pydantic_ai.mcp import MCPServerStreamableHTTP
+from pydantic_ai.models import Model
+
+# Default to the hosted Anthropic model. Override with MODEL, and point at any
+# OpenAI-compatible server (LM Studio, Ollama, vLLM, ...) by setting OPENAI_BASE_URL.
+MODEL = os.environ.get("MODEL", "anthropic:claude-sonnet-4-6")
+OPENAI_BASE_URL = os.environ.get("OPENAI_BASE_URL")
+TANGO_MCP_URL = os.environ.get("TANGO_MCP_URL", "https://govcon.dev/mcp")
+
+
+# --- The output contract -------------------------------------------------------
+# The whole point of using a structured-output agent (vs. the free-text loop in
+# opportunities-agent) is that a webhook-triggered pipeline needs a *typed* result
+# it can route on — not a paragraph. Note what the schema refuses to let the model
+# do: it cannot name a single incumbent. It can only rank candidates, and it must
+# be able to say "this is new work."
+
+class Candidate(BaseModel):
+    """One suspected incumbent. Never the answer on its own — always one of a ranked list."""
+
+    vendor_name: str
+    uei: str | None = Field(None, description="UEI if resolved, else null.")
+    confidence: Literal["high", "medium", "low"]
+    prior_award_value: str | None = Field(
+        None,
+        description="Human-readable. Distinguish task-order obligation from IDV ceiling, "
+        "e.g. '$4.2M obligated under a $50M-ceiling IDV'.",
+    )
+    recompete_date: str | None = Field(
+        None,
+        description="When this incumbent's clock runs out — PoP end or IDV last_date_to_order (ISO).",
+    )
+    evidence: list[str] = Field(
+        default_factory=list,
+        description="The contract PIIDs / IDV keys this candidate is inferred from. "
+        "A candidate with no evidence is a guess and should be dropped.",
+    )
+    why: str = Field(description="One line: which signal carried this — expiry, scope match, magnitude.")
+
+
+class FundingSignal(BaseModel):
+    """The 'is the money real' read. Pivots on the same office/agency as the incumbent."""
+
+    account: str | None = Field(None, description="Federal account symbol / title, if one is identifiable.")
+    enacted_yoy: str | None = None
+    obligated_yoy: str | None = None
+    read: str | None = Field(
+        None,
+        description="Plain-English, e.g. 'enacted +8%, obligations -12% — appropriated but not yet flowing'.",
+    )
+
+
+class IncumbentBrief(BaseModel):
+    solicitation_number: str
+    office: str | None = Field(None, description="The contracting office the incumbent search pivoted on.")
+    new_work_likely: bool = Field(
+        description="True when no candidate clears the expiry-window/scope bar — i.e. likely a first-time "
+        "buy, previously in-house, or otherwise no clear predecessor. When true, candidates may be empty."
+    )
+    candidates: list[Candidate] = Field(
+        default_factory=list, description="Ranked shortlist, best first. At most 3. May be empty if new_work_likely."
+    )
+    funding: FundingSignal | None = None
+    vulnerabilities: list[str] = Field(
+        default_factory=list,
+        description="Capture angles: scope expansion vs. prior award, set-aside change, no incumbent IDV, "
+        "thinning pipeline, shrinking funding account.",
+    )
+    notes: str = Field("", description="Anything that qualifies the brief — thin data, coarse office, ambiguity.")
+
+
+# --- The doctrine --------------------------------------------------------------
+# This is the part that isn't in the API docs. It encodes the office-pivot heuristic
+# we arrived at the hard way: a fresh recompete gets a NEW solicitation number, so you
+# can never join the notice to the prior award on the number. You pivot on the office.
+
+INSTRUCTIONS = """\
+You research likely incumbents on newly posted federal opportunities, using the Tango MCP tools.
+
+Hard rule — do NOT join on the solicitation number. A fresh recompete gets a brand-new
+solicitation number; the prior award carries the OLD one. They never match. Pivot on the
+CONTRACTING OFFICE instead.
+
+Method:
+1. Resolve the office. Use the most specific awarding org on the notice (office level, not just
+   the parent department — department-level filtering returns noise). `resolve` and
+   `get_details(type=organization)` help. The solicitation-number prefix can cross-check the office
+   but is unreliable (civilian formats vary; GWAC/Schedule orders show the ordering office).
+2. Find candidates. `search(type=contract)` and `search(type=idv)` filtered by that office plus the
+   notice's NAICS (and PSC if present). Do NOT just take the most recent awards — recency surfaces
+   brand-new awards, which are the OPPOSITE of a recompete. The incumbent is whoever's contract is
+   EXPIRING: rank by period-of-performance end / IDV last_date_to_order landing near the notice
+   (roughly the notice date minus 12 months through plus 12 months), then by scope match between the
+   notice description and the award description, then by plausible dollar magnitude and set-aside continuity.
+   A vendor merely appearing often at the agency is NOT evidence of incumbency on this scope.
+3. Produce a RANKED shortlist of at most 3 — never assert a single incumbent. Each candidate must carry
+   the contract/IDV identifiers it was inferred from as evidence; drop any candidate you can't back with
+   an identifier.
+4. If nothing clears the expiry-window/scope bar, set new_work_likely=true and return few or no candidates.
+   "Likely new work / no clear predecessor" is a correct and useful answer — do not force a guess.
+5. For the top candidate, optionally pull get_details(type=entity, include_related=true) for size,
+   certifications, total obligations with this agency, and IDV/vehicle holdings.
+6. Funding signal: identify the budget account funding this kind of work at the office/agency and report
+   its enacted-vs-obligated year-over-year movement (get_details(type=budget_account)). Frame it as a
+   funding-health read, not a claim that this specific opportunity is funded from that line.
+
+Be honest about confidence. A coarse office, a wide NAICS, or thin results all mean LOW confidence —
+say so in `notes` rather than inflating the shortlist.
+"""
+
+
+def build_model() -> str | Model:
+    """A model spec for the Agent. A plain provider:id string for hosted models, or an
+    OpenAI-compatible Model when OPENAI_BASE_URL points at a local/self-hosted server."""
+    if not OPENAI_BASE_URL:
+        return MODEL
+    from pydantic_ai.models.openai import OpenAIChatModel
+    from pydantic_ai.providers.openai import OpenAIProvider
+
+    # Strip a leading "openai:" if present — the provider already implies it.
+    model_id = MODEL.split(":", 1)[1] if MODEL.startswith("openai:") else MODEL
+    provider = OpenAIProvider(base_url=OPENAI_BASE_URL, api_key=os.environ.get("OPENAI_API_KEY", "not-needed"))
+    return OpenAIChatModel(model_id, provider=provider)
+
+
+def build_agent() -> Agent[None, IncumbentBrief]:
+    api_key = os.environ["TANGO_API_KEY"]
+    tango_mcp = MCPServerStreamableHTTP(TANGO_MCP_URL, headers={"X-Tango-API-Key": api_key})
+    return Agent(
+        build_model(),
+        toolsets=[tango_mcp],
+        output_type=IncumbentBrief,
+        instructions=INSTRUCTIONS,
+        defer_model_check=True,  # tolerate model ids newer than this pydantic-ai release
+    )
+
+
+def _notice_prompt(notice: dict) -> str:
+    """Hand the model the notice as-is. We define the input shape; the README documents it."""
+    return "A new federal opportunity was just posted. Research its likely incumbent(s) and funding.\n\n" + json.dumps(
+        notice, indent=2, default=str
+    )
+
+
+async def run(notice: dict) -> IncumbentBrief:
+    agent = build_agent()
+    async with agent:  # opens the MCP connection for the duration of the run
+        result = await agent.run(_notice_prompt(notice))
+    return result.output
+
+
+def main() -> None:
+    required = ["TANGO_API_KEY"]
+    if not OPENAI_BASE_URL:  # hosted Anthropic default needs a key; a local server doesn't
+        required.append("ANTHROPIC_API_KEY")
+    for var in required:
+        if not os.environ.get(var):
+            sys.exit(f"missing {var} — see .env.example")
+
+    here = Path(__file__).parent
+    notice_path = Path(sys.argv[1]) if len(sys.argv) > 1 else here / "sample_notice.json"
+    notice = json.loads(notice_path.read_text())
+
+    print(f"\n> incumbent-on-post: {notice.get('solicitation_number', '?')} — {notice.get('title', '')}\n")
+    brief = asyncio.run(run(notice))
+    print(json.dumps(brief.model_dump(), indent=2, default=str))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/incumbent-on-post/demo.svg b/examples/incumbent-on-post/demo.svg
new file mode 100644
index 0000000..93079fb
--- /dev/null
+++ b/examples/incumbent-on-post/demo.svg
@@ -0,0 +1,68 @@
+<svg viewBox="0 0 860 612" xmlns="http://www.w3.org/2000/svg" font-family="-apple-system, Segoe UI, Roboto, Helvetica, Arial, sans-serif" role="img" aria-labelledby="ttl dsc">
+  <title id="ttl">incumbent-on-post pipeline</title>
+  <desc id="dsc">One new SAM.gov notice is pivoted on the contracting office, run through a deterministic scorer and a PydanticAI agent over the Tango MCP, and resolved into a typed ranked IncumbentBrief.</desc>
+  <defs>
+    <marker id="arw" viewBox="0 0 10 10" refX="8" refY="5" markerWidth="7" markerHeight="7" orient="auto-start-reverse">
+      <path d="M0 0 L10 5 L0 10 z" fill="#888780"/>
+    </marker>
+  </defs>
+
+  <rect x="0" y="0" width="860" height="612" fill="#FAFAF7"/>
+
+  <text x="32" y="40" font-size="17" font-weight="500" fill="#2C2C2A">incumbent-on-post</text>
+  <text x="32" y="59" font-size="12" fill="#5F5E5A">one new notice, three answers — who's the incumbent, what was the award, is the money still flowing</text>
+
+  <rect x="280" y="78" width="300" height="56" rx="8" fill="#F1EFE8" stroke="#B4B2A9"/>
+  <text x="430" y="101" font-size="13" font-weight="500" fill="#2C2C2A" text-anchor="middle">new SAM.gov notice</text>
+  <text x="430" y="120" font-size="11" fill="#5F5E5A" text-anchor="middle">36C25026R0042 · VA NCO 5 · NAICS 561492 · SDVOSBC</text>
+
+  <line x1="430" y1="134" x2="430" y2="154" stroke="#888780" stroke-width="1.5" marker-end="url(#arw)"/>
+
+  <rect x="168" y="158" width="524" height="46" rx="8" fill="#FAEEDA" stroke="#EF9F27"/>
+  <text x="430" y="180" font-size="12.5" font-weight="500" fill="#633806" text-anchor="middle">fresh solicitation # never matches the prior award #</text>
+  <text x="430" y="197" font-size="11.5" fill="#854F0B" text-anchor="middle">so pivot on the contracting office — the thing that persists across a recompete</text>
+
+  <line x1="430" y1="204" x2="262" y2="234" stroke="#888780" stroke-width="1.5" marker-end="url(#arw)"/>
+  <line x1="430" y1="204" x2="598" y2="234" stroke="#888780" stroke-width="1.5" marker-end="url(#arw)"/>
+
+  <rect x="112" y="236" width="300" height="98" rx="8" fill="#E1F5EE" stroke="#5DCAA5"/>
+  <text x="130" y="260" font-size="12.5" font-weight="500" fill="#085041">deterministic scorer · scorer.py</text>
+  <text x="130" y="282" font-size="11.5" fill="#0F6E56">rank by expiry window × dollars × recurrence</text>
+  <text x="130" y="301" font-size="11.5" fill="#0F6E56">bounded server-side: expiring_gte / lte</text>
+  <text x="130" y="320" font-size="11.5" fill="#0F6E56">reproducible · eval-able · no LLM</text>
+
+  <rect x="448" y="236" width="300" height="98" rx="8" fill="#EEEDFE" stroke="#AFA9EC"/>
+  <text x="466" y="260" font-size="12.5" font-weight="500" fill="#3C3489">PydanticAI agent → Tango MCP · brief.py</text>
+  <text x="466" y="282" font-size="11.5" fill="#534AB7">resolve · search · get_details</text>
+  <text x="466" y="301" font-size="11.5" fill="#534AB7">scope-match judgment over the PWS text</text>
+  <text x="466" y="320" font-size="11.5" fill="#534AB7">typed output · the repo's first MCP wiring</text>
+
+  <line x1="262" y1="334" x2="404" y2="368" stroke="#888780" stroke-width="1.5" marker-end="url(#arw)"/>
+  <line x1="598" y1="334" x2="456" y2="368" stroke="#888780" stroke-width="1.5" marker-end="url(#arw)"/>
+  <text x="430" y="356" font-size="10.5" fill="#5F5E5A" text-anchor="middle">both independently agree on the top pick</text>
+
+  <rect x="150" y="372" width="560" height="208" rx="10" fill="#FFFFFF" stroke="#B4B2A9"/>
+  <text x="170" y="398" font-size="14" font-weight="500" fill="#2C2C2A">IncumbentBrief</text>
+  <rect x="556" y="385" width="134" height="20" rx="10" fill="#F1EFE8" stroke="#D3D1C7"/>
+  <text x="623" y="399" font-size="10.5" fill="#444441" text-anchor="middle">ranked · never one name</text>
+
+  <text x="172" y="428" font-size="12.5" fill="#2C2C2A">1 · Vet Reporting LLC</text>
+  <rect x="330" y="416" width="46" height="18" rx="9" fill="#EAF3DE"/>
+  <text x="353" y="429" font-size="10.5" font-weight="500" fill="#27500A" text-anchor="middle">high</text>
+  <text x="392" y="428" font-size="11" fill="#5F5E5A">~$500K IDIQ 36C24524A0013 · recompete 2029-02-07</text>
+
+  <text x="172" y="458" font-size="12.5" fill="#2C2C2A">2 · Jamison Professional Services</text>
+  <rect x="330" y="446" width="62" height="18" rx="9" fill="#FAEEDA"/>
+  <text x="361" y="459" font-size="10.5" font-weight="500" fill="#854F0B" text-anchor="middle">medium</text>
+  <text x="402" y="458" font-size="11" fill="#5F5E5A">parallel VISN 5 court-reporter IDIQ</text>
+
+  <text x="172" y="488" font-size="12.5" fill="#2C2C2A">3 · The Ravens Group</text>
+  <rect x="330" y="476" width="46" height="18" rx="9" fill="#E8E6DE"/>
+  <text x="353" y="489" font-size="10.5" font-weight="500" fill="#444441" text-anchor="middle">low</text>
+  <text x="392" y="488" font-size="11" fill="#5F5E5A">SAC Frederick bridge to Nov 2026</text>
+
+  <line x1="170" y1="506" x2="690" y2="506" stroke="#E8E6DE" stroke-width="1"/>
+  <rect x="170" y="516" width="3" height="16" fill="#1D9E75"/>
+  <text x="182" y="528" font-size="11.5" fill="#0F6E56">funding · VHA Medical Support &amp; Compliance 036-0152 · enacted −3% · obligating on pace</text>
+  <text x="170" y="554" font-size="11" fill="#5F5E5A">or — likely new work · no clear predecessor (a valid, first-class answer)</text>
+</svg>
diff --git a/examples/incumbent-on-post/evals.py b/examples/incumbent-on-post/evals.py
new file mode 100644
index 0000000..f0d10a1
--- /dev/null
+++ b/examples/incumbent-on-post/evals.py
@@ -0,0 +1,101 @@
+"""
+Incumbent-on-post — evals for the deterministic scorer.
+
+The opportunities-agent README says the interesting work is upstream: tool design,
+prompt, evals. This is the evals half for *this* recipe. It doesn't grade the LLM
+(that's non-deterministic and paid). It grades the part you actually want pinned —
+the scorer — on invariants that should hold no matter how the underlying data drifts.
+
+These are structural, not "vendor X is the incumbent" (that rots as data changes):
+  - ranked output is sorted by score, descending
+  - every candidate is backed by at least one evidence id (no evidence-free guesses)
+  - an office/NAICS with no expiring work returns [] — the "new work" answer
+  - scores are finite and non-negative
+
+Seed this set, then add a case every time the scorer surprises you. That's how you
+find out whether the ranking is good enough before you wire it to a push notification.
+
+Run:
+    just incumbent-evals
+Requires TANGO_API_KEY (live API; no LLM key needed).
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+from datetime import date
+
+from tango import TangoClient
+
+from scorer import rank_incumbents
+
+# (label, office_code, naics, psc, notice_date, expectation)
+#   expectation: "candidates" -> expect a ranked non-empty list
+#                "new_work"   -> expect [] (no predecessor in this office/category/window)
+CASES = [
+    ("VA NCO 5, court reporting", "36C250", "561492", None, date(2026, 6, 18), "candidates"),
+    ("VA NCO 5, IT services (broad)", "36C250", "541512", None, date(2026, 6, 18), "candidates"),
+    # Valid office, but a recompete window a decade out — nothing expires there, so the
+    # scorer should return []. This exercises the "no predecessor" path without relying
+    # on an invalid agency code (the API rejects those loudly, which is correct).
+    ("VA NCO 5, far-future window", "36C250", "561492", None, date(2035, 1, 1), "new_work"),
+]
+
+
+def check(label: str, cands: list, expectation: str) -> list[str]:
+    failures: list[str] = []
+
+    # Invariant 1: sorted by score, descending.
+    scores = [c.score for c in cands]
+    if scores != sorted(scores, reverse=True):
+        failures.append(f"not sorted descending: {scores}")
+
+    # Invariant 2: every candidate is backed by evidence.
+    for c in cands:
+        if not c.evidence:
+            failures.append(f"candidate {c.vendor_name!r} has no evidence")
+
+    # Invariant 3: scores finite and non-negative.
+    for c in cands:
+        if not (c.score >= 0):
+            failures.append(f"candidate {c.vendor_name!r} has bad score {c.score}")
+
+    # Invariant 4: the expectation about presence/absence.
+    if expectation == "candidates" and not cands:
+        failures.append("expected candidates, got none")
+    if expectation == "new_work" and cands:
+        failures.append(f"expected new_work (empty), got {len(cands)} candidate(s)")
+
+    return failures
+
+
+def main() -> int:
+    if not os.environ.get("TANGO_API_KEY"):
+        sys.exit("missing TANGO_API_KEY — see .env.example")
+
+    tango = TangoClient()
+    total_failures = 0
+
+    for label, office, naics, psc, notice_date, expectation in CASES:
+        cands = rank_incumbents(tango, office_code=office, naics=naics, psc=psc, notice_date=notice_date)
+        failures = check(label, cands, expectation)
+        if failures:
+            total_failures += len(failures)
+            print(f"✗ {label}")
+            for f in failures:
+                print(f"    - {f}")
+        else:
+            top = cands[0].vendor_name if cands else "(none — new work)"
+            print(f"✓ {label}  →  {len(cands)} candidate(s), top: {top}")
+
+    print()
+    if total_failures:
+        print(f"{total_failures} invariant failure(s)")
+        return 1
+    print("all invariants hold")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/examples/incumbent-on-post/sample_notice.json b/examples/incumbent-on-post/sample_notice.json
new file mode 100644
index 0000000..b3a9be1
--- /dev/null
+++ b/examples/incumbent-on-post/sample_notice.json
@@ -0,0 +1,15 @@
+{
+  "solicitation_number": "36C25026R0042",
+  "title": "Court reporting and transcription services",
+  "notice_type": "k",
+  "agency": "Department of Veterans Affairs",
+  "office_code": "36C250",
+  "office": "Network Contracting Office 5 (NCO 5)",
+  "naics": "561492",
+  "psc": "R607",
+  "set_aside": "SDVOSBC",
+  "response_deadline": "2026-07-20",
+  "posted_date": "2026-06-18",
+  "place_of_performance": "Baltimore, MD",
+  "description": "The VA Maryland Health Care System requires verbatim court reporting and certified transcription services for administrative hearings and fact-findings. Base year plus four option years."
+}
diff --git a/examples/incumbent-on-post/scorer.py b/examples/incumbent-on-post/scorer.py
new file mode 100644
index 0000000..d9bf75b
--- /dev/null
+++ b/examples/incumbent-on-post/scorer.py
@@ -0,0 +1,171 @@
+"""
+Incumbent-on-post — the deterministic scorer.
+
+The agent in brief.py gives you a *plausible* incumbent. This module gives you a
+*reproducible* one. It does the one thing you don't want an LLM improvising on a
+webhook-triggered pipeline: ranking candidate prior awards by the recompete signal.
+
+It pivots on the contracting office (not the solicitation number — see brief.py for
+why) and bounds the search to a recompete window server-side using the SDK's
+`expiring_gte/lte` and `last_date_to_order_gte/lte` filters. The ranking is a plain
+scored function: window membership, dollar magnitude, and how often a vendor recurs
+in the expiring set. No model, no network non-determinism beyond the API itself —
+so you can put it under evals.py and notice when it regresses.
+
+Intended use: run this first. If it returns confident candidates, you may not need
+the LLM at all. If it comes back thin or empty, that's the signal to fall through to
+the agent's scope-match judgment — or to report "likely new work."
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from datetime import date, timedelta
+from decimal import Decimal
+from typing import Any
+
+from tango import TangoClient
+
+# How far on either side of the notice an expiring award still counts as "the
+# recompete." Recompetes slip; a year of slack each way is generous but honest.
+RECOMPETE_WINDOW_MONTHS = 12
+
+
+@dataclass
+class Candidate:
+    vendor_name: str
+    uei: str | None
+    prior_award_value: Decimal | None
+    recompete_date: date | None
+    evidence: list[str] = field(default_factory=list)  # PIIDs / IDV keys
+    award_count: int = 0  # how many expiring awards this vendor holds in-window
+    score: float = 0.0
+
+
+def recompete_window(notice_date: date, months: int = RECOMPETE_WINDOW_MONTHS) -> tuple[date, date]:
+    """The band around the notice in which an expiring award reads as the predecessor."""
+    span = timedelta(days=30 * months)
+    return notice_date - span, notice_date + span
+
+
+def _f(row: Any, name: str, default: Any = None) -> Any:
+    """Read a field whether the SDK handed back a dict or a model object."""
+    if isinstance(row, dict):
+        return row.get(name, default)
+    return getattr(row, name, default)
+
+
+def _recipient_name(row: Any) -> str:
+    rec = _f(row, "recipient")
+    if rec is None:
+        return _f(row, "recipient_name", "(unknown)") or "(unknown)"
+    return _f(rec, "display_name", None) or _f(rec, "name", None) or str(rec)
+
+
+def fetch_candidates(
+    tango: TangoClient,
+    *,
+    office_code: str,
+    naics: str | None,
+    psc: str | None,
+    notice_date: date,
+    limit: int = 50,
+) -> list[Any]:
+    """Pull contracts AND IDVs from the office whose clock runs out inside the window.
+
+    `awarding_agency` here is the office code resolved from the notice — pass the most
+    specific org you have, not the parent department. Coarse office in, noise out.
+    """
+    lo, hi = recompete_window(notice_date)
+    rows: list[Any] = []
+
+    contract_filters: dict[str, Any] = {
+        "awarding_agency": office_code,
+        "expiring_gte": lo.isoformat(),
+        "expiring_lte": hi.isoformat(),
+        "limit": limit,
+    }
+    if naics:
+        contract_filters["naics_code"] = naics
+    if psc:
+        contract_filters["psc_code"] = psc
+    rows.extend(tango.list_contracts(**contract_filters).results)
+
+    # IDVs are where task-order recompetes actually live; same window, IDV-native filters.
+    idv_filters: dict[str, Any] = {
+        "awarding_agency": office_code,
+        "last_date_to_order_gte": lo.isoformat(),
+        "last_date_to_order_lte": hi.isoformat(),
+        "limit": limit,
+    }
+    if naics:
+        idv_filters["naics"] = naics
+    if psc:
+        idv_filters["psc"] = psc
+    rows.extend(tango.list_idvs(**idv_filters).results)
+    return rows
+
+
+def score(rows: list[Any], notice_date: date, *, top_n: int = 3) -> list[Candidate]:
+    """Rank vendors in the expiring set. Aggregates by vendor so a prime holding three
+    expiring task orders outranks a one-off — that recurrence IS the incumbency signal.
+
+    Score blends three normalized parts:
+      - proximity: how close the expiry is to the notice (the recompete clock)
+      - magnitude: dollar size, log-damped so a single mega-award doesn't dominate
+      - recurrence: how many expiring awards the vendor holds in-window
+    """
+    if not rows:
+        return []
+
+    by_vendor: dict[str, Candidate] = {}
+    max_value = max((float(_f(r, "total_contract_value") or 0) for r in rows), default=0.0) or 1.0
+    span_days = float(timedelta(days=30 * RECOMPETE_WINDOW_MONTHS).days) or 1.0
+
+    for r in rows:
+        name = _recipient_name(r)
+        rec = _f(r, "recipient")
+        uei = _f(rec, "uei", None) if rec is not None else _f(r, "recipient_uei", None)
+        value = _f(r, "total_contract_value")
+        piid = _f(r, "piid") or _f(r, "key") or "(no id)"
+        expiry = _f(r, "pop_end_date") or _f(r, "last_date_to_order") or _f(r, "expiring")
+        expiry_date = expiry if isinstance(expiry, date) else None
+
+        cand = by_vendor.get(name)
+        if cand is None:
+            cand = Candidate(vendor_name=name, uei=uei, prior_award_value=None, recompete_date=None)
+            by_vendor[name] = cand
+        cand.award_count += 1
+        cand.evidence.append(str(piid))
+        if value is not None:
+            cand.prior_award_value = (cand.prior_award_value or Decimal(0)) + Decimal(str(value))
+        # Keep the expiry closest to the notice as the headline recompete date.
+        if expiry_date and (cand.recompete_date is None or abs((expiry_date - notice_date).days) < abs((cand.recompete_date - notice_date).days)):
+            cand.recompete_date = expiry_date
+
+    from math import log1p
+
+    for cand in by_vendor.values():
+        proximity = 0.0
+        if cand.recompete_date is not None:
+            proximity = max(0.0, 1.0 - abs((cand.recompete_date - notice_date).days) / span_days)
+        magnitude = float(cand.prior_award_value or 0) / max_value
+        recurrence = log1p(cand.award_count)
+        cand.score = round(0.5 * proximity + 0.3 * magnitude + 0.2 * recurrence, 4)
+
+    return sorted(by_vendor.values(), key=lambda c: c.score, reverse=True)[:top_n]
+
+
+def rank_incumbents(
+    tango: TangoClient,
+    *,
+    office_code: str,
+    naics: str | None,
+    psc: str | None,
+    notice_date: date,
+    top_n: int = 3,
+) -> list[Candidate]:
+    """One call: fetch the expiring set for the office and rank it. Empty list is a
+    real answer — it means 'no clear predecessor in this office/category/window'."""
+    rows = fetch_candidates(tango, office_code=office_code, naics=naics, psc=psc, notice_date=notice_date)
+    return score(rows, notice_date, top_n=top_n)
diff --git a/justfile b/justfile
index ea59d71..919bb61 100644
--- a/justfile
+++ b/justfile
@@ -43,6 +43,14 @@ agent *question:
 watch *flags:
     uv run python examples/saved-search-watcher/watcher.py {{flags}}
 
+# Brief the likely incumbent on a new opportunity (PydanticAI agent over the Tango MCP).
+incumbent *notice:
+    uv run python examples/incumbent-on-post/brief.py {{notice}}
+
+# Run the deterministic-scorer invariant evals for incumbent-on-post (no LLM key needed).
+incumbent-evals:
+    uv run python examples/incumbent-on-post/evals.py
+
 # Start the webhook receiver on :8000.
 webhook-serve:
     uv run uvicorn examples.webhook-receiver.server:app --reload --port 8000 --host 0.0.0.0
diff --git a/pyproject.toml b/pyproject.toml
index ab7de3f..4002511 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,6 +18,7 @@ dev = [
 ]
 examples = [
     "anthropic>=0.40",
+    "pydantic-ai-slim[anthropic,mcp,openai]>=1.0",
     "pyyaml>=6.0",
     "fastapi>=0.115",
     "uvicorn>=0.30",
diff --git a/uv.lock b/uv.lock
index ccc6f46..a222903 100644
--- a/uv.lock
+++ b/uv.lock
@@ -6,6 +6,18 @@ resolution-markers = [
     "python_full_version < '3.14'",
 ]
 
+[[package]]
+name = "aiofile"
+version = "3.11.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "caio" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/48/41/2fea7e193e061ce54eacc3b7bc0e6a99e4fcff43c78cf0a76dd781ed8334/aiofile-3.11.1.tar.gz", hash = "sha256:1f91912c6643d2a4e49ca4ae3514f0bf3867ce948a36d99a6411b8f4755f4cf9", size = 19342, upload-time = "2026-05-16T08:18:33.538Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/67/cd/0d76dfc5de72bde52f55f53e925c7d152d9c7906634ec1e0cbc7e8d4ad93/aiofile-3.11.1-py3-none-any.whl", hash = "sha256:ce77d14ac07f77bc2b757834a5c129321f3f705c474593deed5ab209079a52c9", size = 20446, upload-time = "2026-05-16T08:18:32.051Z" },
+]
+
 [[package]]
 name = "annotated-doc"
 version = "0.0.4"
@@ -148,6 +160,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/64/b4/17d4b0b2a2dc85a6df63d1157e028ed19f90d4cd97c36717afef2bc2f395/attrs-26.1.0-py3-none-any.whl", hash = "sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309", size = 67548, upload-time = "2026-03-19T14:22:23.645Z" },
 ]
 
+[[package]]
+name = "authlib"
+version = "1.7.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cryptography" },
+    { name = "joserfc" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/36/98/7d93f30d029643c0275dbc0bd6d5a6f670661ee6c9a94d93af7ab4887600/authlib-1.7.2.tar.gz", hash = "sha256:2cea25fefcd4e7173bdf1372c0afc265c8034b23a8cd5dcb6a9164b826c64231", size = 176511, upload-time = "2026-05-06T08:10:23.116Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fb/95/adcb68e20c34162e9135f370d6e31737719c2b6f94bc953fe7ed1f10fe21/authlib-1.7.2-py2.py3-none-any.whl", hash = "sha256:3e1faedc9d87e7d56a164eca3ccb6ace0d61b94abe83e92242f8dc8bba9b4a9f", size = 259548, upload-time = "2026-05-06T08:10:21.436Z" },
+]
+
 [[package]]
 name = "babel"
 version = "2.18.0"
@@ -157,6 +182,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/77/f5/21d2de20e8b8b0408f0681956ca2c69f1320a3848ac50e6e7f39c6159675/babel-2.18.0-py3-none-any.whl", hash = "sha256:e2b422b277c2b9a9630c1d7903c2a00d0830c409c59ac8cae9081c92f1aeba35", size = 10196845, upload-time = "2026-02-01T12:30:53.445Z" },
 ]
 
+[[package]]
+name = "beartype"
+version = "0.22.9"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/c7/94/1009e248bbfbab11397abca7193bea6626806be9a327d399810d523a07cb/beartype-0.22.9.tar.gz", hash = "sha256:8f82b54aa723a2848a56008d18875f91c1db02c32ef6a62319a002e3e25a975f", size = 1608866, upload-time = "2025-12-13T06:50:30.72Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/71/cc/18245721fa7747065ab478316c7fea7c74777d07f37ae60db2e84f8172e8/beartype-0.22.9-py3-none-any.whl", hash = "sha256:d16c9bbc61ea14637596c5f6fbff2ee99cbe3573e46a716401734ef50c3060c2", size = 1333658, upload-time = "2025-12-13T06:50:28.266Z" },
+]
+
 [[package]]
 name = "beautifulsoup4"
 version = "4.14.3"
@@ -187,6 +221,36 @@ css = [
     { name = "tinycss2" },
 ]
 
+[[package]]
+name = "cachetools"
+version = "7.1.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f4/8b/0d3945a13955303b81272f759a0331e54c5c793da455e6f5706b89d2639c/cachetools-7.1.4.tar.gz", hash = "sha256:437f55a4e0c1b01a4f3077cc470e6991d47430970e36fbcb77e2be0df4fc1cd6", size = 40085, upload-time = "2026-05-21T22:40:43.376Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8c/7b/1fc1c09cc0756cf25861a3be10565915953876da48bb228fb9a672b20a42/cachetools-7.1.4-py3-none-any.whl", hash = "sha256:323dc4127934744db5b54eb4924482d7edafbf9554e820d1531c2e08c0e4ef54", size = 16761, upload-time = "2026-05-21T22:40:41.845Z" },
+]
+
+[[package]]
+name = "caio"
+version = "0.9.25"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/92/88/b8527e1b00c1811db339a1df8bd1ae49d146fcea9d6a5c40e3a80aaeb38d/caio-0.9.25.tar.gz", hash = "sha256:16498e7f81d1d0f5a4c0ad3f2540e65fe25691376e0a5bd367f558067113ed10", size = 26781, upload-time = "2025-12-26T15:21:36.501Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d3/25/79c98ebe12df31548ba4eaf44db11b7cad6b3e7b4203718335620939083c/caio-0.9.25-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:fb7ff95af4c31ad3f03179149aab61097a71fd85e05f89b4786de0359dffd044", size = 36983, upload-time = "2025-12-26T15:21:36.075Z" },
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