google-adk: bring docs to parity with adk-redis 0.0.5

content/integrate/google-adk/_index.md

@@ -42,12 +42,17 @@ docker run -d --name redis -p 6379:6379 redis:8.4-alpine
 docker run -d --name agent-memory-server -p 8088:8088 \
   -e REDIS_URL=redis://host.docker.internal:6379 \
   -e GEMINI_API_KEY=your-key \
-  -e GENERATION_MODEL=gemini/gemini-2.0-flash \
+  -e GENERATION_MODEL=gemini/gemini-2.5-flash \
   -e EMBEDDING_MODEL=gemini/text-embedding-004 \
   redislabs/agent-memory-server:latest \
   agent-memory api --host 0.0.0.0 --port 8088 --task-backend=asyncio
 ```
 
+On Linux, `host.docker.internal` does not resolve by default. Use
+`--network=host` plus `REDIS_URL=redis://127.0.0.1:6379`, or point
+`REDIS_URL` at the Docker bridge gateway (typically
+`redis://172.17.0.1:6379`).
+
 ## Installation
 
 ```bash
@@ -57,11 +62,17 @@ pip install adk-redis[memory]
 # Search tools via RedisVL
 pip install adk-redis[search]
 
+# SQL-style search tool (sql-redis)
+pip install adk-redis[sql]
+
 # Managed semantic caching via LangCache
 pip install adk-redis[langcache]
 
 # Everything
 pip install adk-redis[all]
+
+# For the RedisVL MCP server (used with ADK's native McpToolset)
+pip install 'redisvl[mcp]>=0.18.2'
 ```
 
 ## Quick start
@@ -118,9 +129,9 @@ runner = Runner(
 |------------|-------------|------|
 | **Redis Agent Memory** | Working and long-term memory via framework services, REST tools, or MCP | [Redis Agent Memory]({{< relref "/integrate/google-adk/redis-agent-memory" >}}) |
 | **Integration patterns** | Framework-managed, LLM-controlled REST, and MCP tools | [Integration patterns]({{< relref "/integrate/google-adk/integration-patterns" >}}) |
-| **Search tools** | Vector, hybrid, text, and range search via RedisVL | [Search tools]({{< relref "/integrate/google-adk/search-tools" >}}) |
+| **Search tools** | Vector, hybrid, text, range, and SQL search via RedisVL, plus the `rvl mcp` server over `McpToolset` | [Search tools]({{< relref "/integrate/google-adk/search-tools" >}}) |
 | **Semantic caching** | LLM response and tool result caching | [Semantic caching]({{< relref "/integrate/google-adk/semantic-caching" >}}) |
-| **Examples** | Seven complete examples covering all capabilities | [Examples]({{< relref "/integrate/google-adk/examples" >}}) |
+| **Examples** | Nine complete examples covering all capabilities | [Examples]({{< relref "/integrate/google-adk/examples" >}}) |
 
 ## More info
 

content/integrate/google-adk/examples.md

@@ -11,13 +11,13 @@ categories:
 description: Complete examples for every adk-redis capability.
 group: ai
 stack: true
-summary: Seven runnable examples covering Redis Agent Memory, search tools, semantic
+summary: Nine runnable examples covering Redis Agent Memory, search tools, semantic
   caching, and MCP integration.
 type: integration
 weight: 5
 ---
 
-The [adk-redis repository](https://github.com/redis-developer/adk-redis/tree/main/examples) includes seven complete examples. Each focuses on a specific capability.
+The [adk-redis repository](https://github.com/redis-developer/adk-redis/tree/main/examples) includes nine complete examples. Each focuses on a specific capability.
 
 ## Prerequisites
 
@@ -64,10 +64,26 @@ Demonstrates MCP-based memory integration. The agent connects to the Agent Memor
 
 **Capability:** Vector, hybrid, text, and range search
 
-All four RedisVL [search tools]({{< relref "/integrate/google-adk/search-tools" >}}) plugged into a single agent with a product catalog dataset.
+Four in-process RedisVL [search tools]({{< relref "/integrate/google-adk/search-tools" >}}) plugged into a single agent with a product catalog dataset.
 
 [View on GitHub](https://github.com/redis-developer/adk-redis/tree/main/examples/redis_search_tools)
 
+## `redis_sql_search`
+
+**Capability:** SQL `SELECT` search
+
+A 10-product catalog with the `RedisSQLSearchTool`. The agent emits parameterized SQL (`WHERE category = 'electronics' AND price < :max_price`) to answer structured catalog questions. Requires `pip install 'adk-redis[sql]'`.
+
+[View on GitHub](https://github.com/redis-developer/adk-redis/tree/main/examples/redis_sql_search)
+
+## `redisvl_mcp_search`
+
+**Capability:** RedisVL MCP server via ADK's `McpToolset`
+
+The MCP counterpart of `redis_search_tools`. A `rvl mcp` server hosts a knowledge-base index in hybrid (vector + BM25) mode and the agent connects via ADK's native `McpToolset`. No adk-redis wrapper involved; the standard `McpToolset` + `StdioConnectionParams` pattern is used.
+
+[View on GitHub](https://github.com/redis-developer/adk-redis/tree/main/examples/redisvl_mcp_search)
+
 ## `semantic_cache`
 
 **Capability:** Local semantic caching (RedisVL)

content/integrate/google-adk/redis-agent-memory.md

@@ -45,7 +45,7 @@ session_service = RedisWorkingMemorySessionService(
     config=RedisWorkingMemorySessionServiceConfig(
         api_base_url="http://localhost:8088",
         default_namespace="my_app",
-        model_name="gemini-2.0-flash",
+        model_name="gemini-2.5-flash",
         context_window_max=8000,
     )
 )

content/integrate/google-adk/search-tools.md

@@ -8,7 +8,7 @@ categories:
 - oss
 - rs
 - rc
-description: Vector, hybrid, text, and range search tools for Google ADK agents.
+description: Vector, hybrid, text, range, and SQL search tools for Google ADK agents, plus the RedisVL MCP server.
 group: ai
 stack: true
 summary: Add retrieval-augmented generation (RAG) to ADK agents using RedisVL-powered
@@ -17,7 +17,7 @@ type: integration
 weight: 3
 ---
 
-adk-redis provides four search tools that wrap [RedisVL]({{< relref "/develop/ai/redisvl" >}}) query types into ADK-compatible tools. The LLM sees each tool as a callable function with a `query` parameter and gets back structured results.
+adk-redis provides five in-process search tools that wrap [RedisVL]({{< relref "/develop/ai/redisvl" >}}) query types into ADK-compatible tools. The LLM sees each tool as a callable function and gets back structured results. For multi-agent or polyglot deployments, the same RedisVL index can also be served over MCP via the `rvl mcp` server and consumed with ADK's native `McpToolset` (see the [RedisVL MCP server](#redisvl-mcp-server) section below).
 
 ## Overview
 
@@ -27,6 +27,7 @@ adk-redis provides four search tools that wrap [RedisVL]({{< relref "/develop/ai
 | `RedisHybridSearchTool` | Vector + BM25 | Queries with specific terms + concepts |
 | `RedisTextSearchTool` | BM25 keyword | Exact terms, error messages, IDs |
 | `RedisRangeSearchTool` | Distance threshold | Exhaustive retrieval within a radius |
+| `RedisSQLSearchTool` | SQL `SELECT` | Structured filters (`WHERE`, `BETWEEN`, parameterized) |
 
 ## Vector search
 
@@ -106,6 +107,80 @@ range_tool = RedisRangeSearchTool(
 )
 ```
 
+## SQL search
+
+`RedisSQLSearchTool` wraps `redisvl.query.SQLQuery`. The LLM emits a SQL `SELECT` statement (with optional `:name` parameter placeholders) and the tool translates it into the right `FT.SEARCH` or `FT.AGGREGATE` call. Best for structured filters: tag equality, numeric ranges, multi-predicate `WHERE` clauses. Requires `pip install 'adk-redis[sql]'`.
+
+```python
+from adk_redis import RedisSQLSearchTool
+
+sql_tool = RedisSQLSearchTool(
+    index=index,
+    name="catalog_sql_search",
+    description=(
+        "Run a SQL SELECT against the product catalog. "
+        "Use :name placeholders for values."
+    ),
+)
+```
+
+The LLM might emit a call like:
+
+```sql
+SELECT title, brand, price
+FROM products
+WHERE category = 'electronics' AND price < :max_price
+```
+
+with `params={"max_price": 100}`. See the [redis_sql_search example](https://github.com/redis-developer/adk-redis/tree/main/examples/redis_sql_search) for a runnable demo.
+
+## RedisVL MCP server
+
+The five tools above run in-process against a Python `SearchIndex`. To serve one Redis index to multiple agents (Python, JS, Claude Desktop), or to put server-side guardrails like `--read-only` and bearer auth between the agent and the index, run RedisVL's MCP server (`rvl mcp`) and connect ADK's native `McpToolset` to it.
+
+```python
+from google.adk import Agent
+from google.adk.tools.mcp_tool import McpToolset
+from google.adk.tools.mcp_tool.mcp_session_manager import StdioConnectionParams
+from mcp import StdioServerParameters
+
+agent = Agent(
+    model="gemini-2.5-flash",
+    name="redis_mcp_agent",
+    instruction="Use the search-records tool to answer questions.",
+    tools=[
+        McpToolset(
+            connection_params=StdioConnectionParams(
+                server_params=StdioServerParameters(
+                    command="rvl",
+                    args=[
+                        "mcp",
+                        "--config",
+                        "/path/to/mcp_config.yaml",
+                        "--read-only",
+                    ],
+                ),
+                timeout=30,
+            ),
+            tool_filter=["search-records"],
+        ),
+    ],
+)
+```
+
+The server is configured per index via a YAML file and exposes two tools: `search-records` (vector / fulltext / hybrid, chosen at server start, with schema-aware filter and return-field hints) and `upsert-records` (suppress with `--read-only`). Supports `stdio`, `sse`, and `streamable-http` transports; bearer auth on HTTP.
+
+Install the CLI with `pip install 'redisvl[mcp]>=0.18.2'`. For a runnable demo, see the [redisvl_mcp_search example](https://github.com/redis-developer/adk-redis/tree/main/examples/redisvl_mcp_search).
+
+### When to choose in-process vs MCP
+
+| Path | Use when |
+|------|----------|
+| In-process tools (above) | Single Python agent, fast onboarding, complex Python-side `FilterExpression` composition. |
+| MCP server | Multi-agent or polyglot deployments, server-side ops gates, schema-aware tool descriptions. |
+
+Range and SQL search have no MCP equivalent today; use the in-process tools for either.
+
 ## Multi-tool agent
 
 Wire multiple search tools into a single agent and let the LLM choose the right one:
@@ -129,5 +204,7 @@ The `name` and `description` on each tool matter: the LLM reads them to decide w
 
 ## More info
 
-- [redis_search_tools example](https://github.com/redis-developer/adk-redis/tree/main/examples/redis_search_tools): All four search tools with a product catalog
+- [redis_search_tools example](https://github.com/redis-developer/adk-redis/tree/main/examples/redis_search_tools): vector, text, and range tools with a product catalog
+- [redis_sql_search example](https://github.com/redis-developer/adk-redis/tree/main/examples/redis_sql_search): SQL `SELECT` against a bound index
+- [redisvl_mcp_search example](https://github.com/redis-developer/adk-redis/tree/main/examples/redisvl_mcp_search): same corpus served via `rvl mcp` + ADK `McpToolset`
 - [RedisVL documentation]({{< relref "/develop/ai/redisvl" >}})

content/integrate/google-adk/semantic-caching.md

@@ -45,7 +45,7 @@ provider = RedisVLCacheProvider(
         ttl=3600,
         distance_threshold=0.1,
     ),
-    vectorizer=HFTextVectorizer(model="redis/langcache-embed-v1"),
+    vectorizer=HFTextVectorizer(model="redis/langcache-embed-v2"),
 )
 ```
 
@@ -89,7 +89,7 @@ before_cb, after_cb = create_llm_cache_callbacks(llm_cache)
 
 agent = Agent(
     name="cached_agent",
-    model="gemini-2.0-flash",
+    model="gemini-2.5-flash",
     instruction="You are a helpful assistant.",
     before_model_callback=before_cb,
     after_model_callback=after_cb,