mirror of
https://github.com/google/adk-docs.git
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Kristopher Overholt
141 lines
4.7 KiB
Markdown
141 lines
4.7 KiB
Markdown
---
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catalog_title: Pinecone
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catalog_description: Store data, perform semantic search, and rerank results
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catalog_icon: /integrations/assets/pinecone.png
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catalog_tags: ["data","mcp"]
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---
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# Pinecone MCP tool for ADK
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<div class="language-support-tag">
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<span class="lst-supported">Supported in ADK</span><span class="lst-python">Python</span><span class="lst-typescript">TypeScript</span>
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</div>
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The [Pinecone MCP Server](https://github.com/pinecone-io/pinecone-mcp)
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connects your ADK agent to [Pinecone](https://www.pinecone.io/), a vector
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database for AI applications. This integration gives your agent the ability to
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manage indexes, store and search data using semantic search with metadata
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filtering, and search across multiple indexes with reranking.
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## Use cases
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- **Semantic Search and Retrieval**: Search stored data using natural language
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queries with metadata filtering and reranking.
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- **Knowledge Base Management**: Store and manage data to build and maintain
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retrieval-augmented generation (RAG) systems.
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- **Cross-Index Search**: Search across multiple Pinecone indexes
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simultaneously, with automatic deduplication and reranking of results.
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## Prerequisites
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- A [Pinecone](https://www.pinecone.io/) account
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- An API key from the [Pinecone Console](https://app.pinecone.io)
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## Use with agent
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=== "Python"
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=== "Local MCP Server"
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```python
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from google.adk.agents import Agent
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from google.adk.tools.mcp_tool import McpToolset
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from google.adk.tools.mcp_tool.mcp_session_manager import StdioConnectionParams
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from mcp import StdioServerParameters
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PINECONE_API_KEY = "YOUR_PINECONE_API_KEY"
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root_agent = Agent(
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model="gemini-flash-latest",
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name="pinecone_agent",
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instruction="Help users manage and search their Pinecone vector indexes",
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tools=[
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McpToolset(
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connection_params=StdioConnectionParams(
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server_params=StdioServerParameters(
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command="npx",
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args=[
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"-y",
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"@pinecone-database/mcp",
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],
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env={
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"PINECONE_API_KEY": PINECONE_API_KEY,
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}
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),
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timeout=30,
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),
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)
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],
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)
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```
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=== "TypeScript"
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=== "Local MCP Server"
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```typescript
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import { LlmAgent, MCPToolset } from "@google/adk";
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const PINECONE_API_KEY = "YOUR_PINECONE_API_KEY";
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const rootAgent = new LlmAgent({
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model: "gemini-flash-latest",
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name: "pinecone_agent",
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instruction: "Help users manage and search their Pinecone vector indexes",
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tools: [
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new MCPToolset({
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type: "StdioConnectionParams",
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serverParams: {
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command: "npx",
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args: ["-y", "@pinecone-database/mcp"],
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env: {
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PINECONE_API_KEY: PINECONE_API_KEY,
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},
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},
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}),
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],
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});
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export { rootAgent };
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```
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!!! note
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Only indexes with [integrated inference](https://docs.pinecone.io/guides/inference/understanding-inference)
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are supported. Indexes without an integrated embedding model are not
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supported by this MCP server.
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## Available tools
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### Documentation
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Tool | Description
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---- | -----------
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`search-docs` | Search the official Pinecone documentation
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### Index management
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Tool | Description
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---- | -----------
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`list-indexes` | List all Pinecone indexes
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`describe-index` | Describe the configuration of an index
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`describe-index-stats` | Get statistics about an index, including record count and available namespaces
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`create-index-for-model` | Create a new index with an integrated inference model for embedding
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### Data operations
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Tool | Description
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---- | -----------
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`upsert-records` | Insert or update records in an index with integrated inference
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`search-records` | Search for records using a text query with options for metadata filtering and reranking
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`cascading-search` | Search across multiple indexes, deduplicating and reranking the results
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`rerank-documents` | Rerank a collection of records or text documents using a specialized reranking model
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## Additional resources
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- [Pinecone MCP Server Repository](https://github.com/pinecone-io/pinecone-mcp)
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- [Pinecone MCP Documentation](https://docs.pinecone.io/guides/operations/mcp-server)
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- [Pinecone Documentation](https://docs.pinecone.io)
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