From d0fda56fc4684ca6f695bc3d57869343ada11460 Mon Sep 17 00:00:00 2001 From: "V. David Zvenyach" Date: Thu, 18 Jun 2026 19:23:49 -0500 Subject: [PATCH 1/3] Add incumbent-on-post example: agent + MCP, scorer, evals MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit When a new opportunity posts, brief the likely incumbent, prior award, and funding from one notice. This is the cookbook's first MCP integration — a PydanticAI agent drives the hosted Tango MCP rather than hand-wiring the SDK. The recipe is built around an honest arc: a fresh recompete gets a new solicitation number, so it never joins to the prior award on the number. The pivot is the contracting office, and the incumbent is whoever's award is expiring near the notice — an inference, so the output is a ranked shortlist with confidence and evidence, never a single asserted name, with "likely new work" as a first-class answer. - brief.py: PydanticAI agent over the hosted Tango MCP, returning a typed IncumbentBrief (the easy version). - scorer.py: deterministic candidate ranking (expiry-window x dollars x recurrence), bounded server-side — reproducible and eval-able (the honest version). - evals.py: structural invariants on the scorer; runs without an LLM key. - demo.svg: self-contained pipeline diagram built from a real run. - just incumbent / just incumbent-evals; pydantic-ai-slim added to the examples group; README tables updated. Co-Authored-By: Claude Opus 4.8 --- README.md | 1 + examples/README.md | 1 + examples/incumbent-on-post/README.md | 67 ++ examples/incumbent-on-post/brief.py | 186 ++++++ examples/incumbent-on-post/demo.svg | 68 +++ examples/incumbent-on-post/evals.py | 101 +++ examples/incumbent-on-post/sample_notice.json | 15 + examples/incumbent-on-post/scorer.py | 171 ++++++ justfile | 8 + pyproject.toml | 1 + uv.lock | 578 ++++++++++++++++++ 11 files changed, 1197 insertions(+) create mode 100644 examples/incumbent-on-post/README.md create mode 100644 examples/incumbent-on-post/brief.py create mode 100644 examples/incumbent-on-post/demo.svg create mode 100644 examples/incumbent-on-post/evals.py create mode 100644 examples/incumbent-on-post/sample_notice.json create mode 100644 examples/incumbent-on-post/scorer.py 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..a29055f --- /dev/null +++ b/examples/incumbent-on-post/README.md @@ -0,0 +1,67 @@ +# 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** `ANTHROPIC_API_KEY` (for the model). `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. + +## Hosted vs. local MCP + +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..9339c47 --- /dev/null +++ b/examples/incumbent-on-post/brief.py @@ -0,0 +1,186 @@ +""" +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 ANTHROPIC_API_KEY (for the model). +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 + +MODEL = "anthropic:claude-sonnet-4-6" +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_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( + 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: + for var in ("TANGO_API_KEY", "ANTHROPIC_API_KEY"): + 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 @@ + + incumbent-on-post pipeline + 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. + + + + + + + + + incumbent-on-post + one new notice, three answers — who's the incumbent, what was the award, is the money still flowing + + + new SAM.gov notice + 36C25026R0042 · VA NCO 5 · NAICS 561492 · SDVOSBC + + + + + fresh solicitation # never matches the prior award # + so pivot on the contracting office — the thing that persists across a recompete + + + + + + deterministic scorer · scorer.py + rank by expiry window × dollars × recurrence + bounded server-side: expiring_gte / lte + reproducible · eval-able · no LLM + + + PydanticAI agent → Tango MCP · brief.py + resolve · search · get_details + scope-match judgment over the PWS text + typed output · the repo's first MCP wiring + + + + both independently agree on the top pick + + + IncumbentBrief + + ranked · never one name + + 1 · Vet Reporting LLC + + high + ~$500K IDIQ 36C24524A0013 · recompete 2029-02-07 + + 2 · Jamison Professional Services + + medium + parallel VISN 5 court-reporter IDIQ + + 3 · The Ravens Group + + low + SAC Frederick bridge to Nov 2026 + + + + funding · VHA Medical Support & Compliance 036-0152 · enacted −3% · obligating on pace + or — likely new work · no clear predecessor (a valid, first-class answer) + 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 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David Zvenyach" Date: Thu, 18 Jun 2026 20:24:02 -0500 Subject: [PATCH 2/3] incumbent-on-post: support local / OpenAI-compatible models Let brief.py run against any OpenAI-compatible server (LM Studio, Ollama, vLLM) in addition to the hosted Anthropic default. Set OPENAI_BASE_URL + MODEL to point at a local server; ANTHROPIC_API_KEY is then no longer required. Adds the openai extra to pydantic-ai-slim. Co-Authored-By: Claude Opus 4.8 --- examples/incumbent-on-post/brief.py | 34 +++++- pyproject.toml | 2 +- uv.lock | 174 +++++++++++++++++++++++++++- 3 files changed, 203 insertions(+), 7 deletions(-) diff --git a/examples/incumbent-on-post/brief.py b/examples/incumbent-on-post/brief.py index 9339c47..d614717 100644 --- a/examples/incumbent-on-post/brief.py +++ b/examples/incumbent-on-post/brief.py @@ -20,7 +20,12 @@ just incumbent # uses sample_notice.json just incumbent path/to/notice.json -Requires TANGO_API_KEY (for the MCP server) and ANTHROPIC_API_KEY (for the model). +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. """ @@ -36,8 +41,12 @@ from pydantic import BaseModel, Field from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP +from pydantic_ai.models import Model -MODEL = "anthropic:claude-sonnet-4-6" +# 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") @@ -142,11 +151,25 @@ class IncumbentBrief(BaseModel): """ +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( - MODEL, + build_model(), toolsets=[tango_mcp], output_type=IncumbentBrief, instructions=INSTRUCTIONS, @@ -169,7 +192,10 @@ async def run(notice: dict) -> IncumbentBrief: def main() -> None: 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3e0191fd690736c749eb123761ac65757642be4b Mon Sep 17 00:00:00 2001 From: "V. David Zvenyach" Date: Thu, 18 Jun 2026 20:26:08 -0500 Subject: [PATCH 3/3] incumbent-on-post: document local / OpenAI-compatible model Note the two independent "local" axes (MCP server vs. model) and show how to point the agent at an OpenAI-compatible server via OPENAI_BASE_URL + MODEL. Co-Authored-By: Claude Opus 4.8 --- examples/incumbent-on-post/README.md | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/examples/incumbent-on-post/README.md b/examples/incumbent-on-post/README.md index a29055f..f4eadcf 100644 --- a/examples/incumbent-on-post/README.md +++ b/examples/incumbent-on-post/README.md @@ -41,15 +41,25 @@ 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** `ANTHROPIC_API_KEY` (for the model). `just incumbent-evals` needs only `TANGO_API_KEY`. +`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. -## Hosted vs. local MCP +## Running locally -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. +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