# How to integrate Pinecone MCP with Pydantic AI ```json { "title": "How to integrate Pinecone MCP with Pydantic AI", "toolkit": "Pinecone", "toolkit_slug": "pinecone", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/pinecone/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/pinecone/framework/pydantic-ai.md", "updated_at": "2026-03-29T06:45:37.839Z" } ``` ## Introduction This guide walks you through connecting Pinecone to Pydantic AI using the Composio tool router. By the end, you'll have a working Pinecone agent that can query all vectors similar to user question, upsert document embeddings into a namespace, delete vectors from the archive index through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Pinecone account through Composio's Pinecone MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Pinecone with - [ChatGPT](https://composio.dev/toolkits/pinecone/framework/chatgpt) - [Antigravity](https://composio.dev/toolkits/pinecone/framework/antigravity) - [OpenAI Agents SDK](https://composio.dev/toolkits/pinecone/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/pinecone/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/pinecone/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/pinecone/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/pinecone/framework/codex) - [Cursor](https://composio.dev/toolkits/pinecone/framework/cursor) - [VS Code](https://composio.dev/toolkits/pinecone/framework/vscode) - [OpenCode](https://composio.dev/toolkits/pinecone/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/pinecone/framework/openclaw) - [Hermes](https://composio.dev/toolkits/pinecone/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/pinecone/framework/cli) - [Google ADK](https://composio.dev/toolkits/pinecone/framework/google-adk) - [LangChain](https://composio.dev/toolkits/pinecone/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/pinecone/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/pinecone/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/pinecone/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/pinecone/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up your Composio API key and User ID - How to create a Composio Tool Router session for Pinecone - How to attach an MCP Server to a Pydantic AI agent - How to stream responses and maintain chat history - How to build a simple REPL-style chat interface to test your Pinecone workflows ## What is Pydantic AI? Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications. Key features include: - Type Safety: Built on Pydantic for automatic data validation - MCP Support: Native support for Model Context Protocol servers - Streaming: Built-in support for streaming responses - Async First: Designed for async/await patterns ## What is the Pinecone MCP server, and what's possible with it? The Pinecone MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Pinecone account. It provides structured and secure access so your agent can perform Pinecone operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `PINECONE_CANCEL_BULK_IMPORT` | Cancel Bulk Import | Tool to cancel a bulk import operation in Pinecone. Use when you need to stop an ongoing import operation that is not yet finished. | | `PINECONE_CONFIGURE_INDEX` | Configure Index | Tool to configure an existing Pinecone index, including pod type, replicas, deletion protection, and tags. Use when you need to scale an index vertically or horizontally, enable/disable deletion protection, or update tags. The change is asynchronous; check index status for completion. | | `PINECONE_CREATE_BACKUP` | Create Backup | Tool to create a backup of a Pinecone index for disaster recovery and version control. Use when you need to preserve the current state of an index including vectors, metadata, and configuration. | | `PINECONE_CREATE_INDEX` | Create Index | Tool to create a Pinecone index with specified configuration. Use when you need to initialize a new vector database index for storing and querying embeddings. | | `PINECONE_CREATE_INDEX_WITH_EMBEDDING_MODEL` | Create Index with Embedding Model | Tool to create a Pinecone index with integrated embedding model for automatic vectorization. Use when you need to set up a new index that automatically converts text to vectors using a pre-configured embedding model. | | `PINECONE_CREATE_INDEX_FROM_BACKUP` | Create Index from Backup | Tool to create an index from a backup. Use when you need to restore or duplicate index data from a previously saved backup. | | `PINECONE_CREATE_NAMESPACE` | Create Namespace | Tool to create a namespace within a serverless Pinecone index. Use when you need to organize vectors into isolated partitions. | | `PINECONE_DELETE_INDEX` | Delete Index | Tool to permanently delete a Pinecone index. Use when you need to remove an index from your project. Note: Deletion protection and pending collections can prevent deletion. | | `PINECONE_DELETE_NAMESPACE` | Delete Namespace | Tool to permanently delete a namespace from a serverless index. Use when you need to remove an entire namespace and all its data. This operation is irreversible and only supported on serverless indexes. | | `PINECONE_DESCRIBE_BACKUP` | Describe Backup | Tool to retrieve detailed information about a specific backup. Use when you need to check backup status, configuration, or metadata. | | `PINECONE_DESCRIBE_BULK_IMPORT` | Describe Bulk Import | Tool to describe a specific bulk import operation in Pinecone. Use when you need to retrieve detailed information about an import's status, progress, timing, and any errors. | | `PINECONE_DESCRIBE_INDEX_STATS` | Describe Index Stats | Tool to get index statistics including vector count per namespace, dimensions, and fullness. Use when you need to understand the contents and status of an index. | | `PINECONE_DESCRIBE_RESTORE_JOB` | Describe Restore Job | Tool to get detailed information about a specific restore job in Pinecone. Use when you need to check the status, progress, or metadata of a restore operation. | | `PINECONE_GENERATE_EMBEDDINGS` | Generate Embeddings | Tool to generate vector embeddings for input text using Pinecone's hosted embedding models. Use when you need to convert text into vector representations for semantic search or similarity matching. | | `PINECONE_GET_MODEL_INFORMATION` | Get Model Information | Tool to retrieve detailed information about a specific model hosted by Pinecone. Use when you need to understand model capabilities for embedding and reranking operations. | | `PINECONE_LIST_BULK_IMPORTS` | List Bulk Imports | Tool to list all recent and ongoing bulk import operations in Pinecone. Use when you need to monitor or track the status of data import jobs. Supports pagination with a default limit of 100 imports per page. | | `PINECONE_LIST_COLLECTIONS` | List Collections | Tool to list all collections in a Pinecone project (pod-based indexes only). Use when you need to view available collections. | | `PINECONE_LIST_INDEX_BACKUPS` | List Index Backups | Tool to list all backups for a specific Pinecone index. Use when you need to view available backups for an index. Supports pagination via limit and paginationToken parameters. | | `PINECONE_LIST_INDEXES` | List Indexes | Tool to list all indexes in a Pinecone project. Use when you need to retrieve all indexes with their configurations and status information. | | `PINECONE_LIST_AVAILABLE_MODELS` | List Available Models | Tool to list all available embedding and reranking models hosted by Pinecone. Use when you need to discover available models or filter by model type (embed/rerank) or vector type (dense/sparse). | | `PINECONE_LIST_NAMESPACES` | List Namespaces | Tool to list all namespaces in a serverless Pinecone index. Use when you need to discover available namespaces for data organization. Returns up to 100 namespaces by default with pagination support. | | `PINECONE_LIST_PROJECT_BACKUPS` | List Project Backups | Tool to list all backups for indexes in a Pinecone project. Use when you need to retrieve backup information across all project indexes. Supports pagination with limit and paginationToken parameters. | | `PINECONE_LIST_RESTORE_JOBS` | List Restore Jobs | Tool to list all restore jobs for a project with pagination support. Use when you need to view the status of restore operations or track restore progress. | | `PINECONE_LIST_VECTORS` | List Vectors | Tool to list vector IDs in a Pinecone serverless index. Use when you need to browse or retrieve vector identifiers from a namespace. Supports filtering by prefix and pagination for large result sets. | | `PINECONE_QUERY_VECTORS` | Query Vectors | Tool to perform semantic search within a Pinecone index using a query vector. Retrieves IDs and similarity scores of the most similar items, ordered from most to least similar. Either vector or id parameter must be provided. | | `PINECONE_RERANK_DOCUMENTS` | Rerank Documents | Tool to rerank documents by semantic relevance to a query. Use when you need to order retrieved documents by their semantic relevance to a user's search query using Pinecone's hosted reranking models. | | `PINECONE_SEARCH_RECORDS_IN_NAMESPACE` | Search Records in Namespace | Tool to search records within a Pinecone namespace using text, vector, or ID query. Use when you need to find similar records based on embeddings or record IDs. Results can optionally be reranked for relevance. | | `PINECONE_START_BULK_IMPORT` | Start Bulk Import | Tool to start an asynchronous bulk import of vectors from object storage (S3, GCS, or Azure Blob Storage) into a Pinecone index. Use when you need to import large volumes of vectors from external storage. Returns an import ID to track the operation status. | | `PINECONE_UPDATE_VECTOR` | Update Vector | Tool to update a vector in Pinecone by ID. Use to overwrite vector values and/or metadata. Supports bulk updates via metadata filters. | | `PINECONE_UPSERT_RECORDS_TO_NAMESPACE` | Upsert Records to Namespace | Tool to upsert text records into a Pinecone namespace. Use when you need to add or update records with automatic text-to-vector conversion. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Pinecone MCP server is an implementation of the Model Context Protocol that connects your AI agent to Pinecone. It provides structured and secure access so your agent can perform Pinecone operations on your behalf through a secure, permission-based interface. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites Before starting, make sure you have: - Python 3.9 or higher - A Composio account with an active API key - Basic familiarity with Python and async programming ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider. - Keep the API key safe. Composio API Key - Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies Install the required libraries. What's happening: - composio connects your agent to external SaaS tools like Pinecone - pydantic-ai lets you create structured AI agents with tool support - python-dotenv loads your environment variables securely from a .env file ```bash pip install composio pydantic-ai python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates your agent to Composio's API - USER_ID associates your session with your account for secure tool access - OPENAI_API_KEY to access OpenAI LLMs ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key ``` ### 4. Import dependencies What's happening: - We load environment variables and import required modules - Composio manages connections to Pinecone - MCPServerStreamableHTTP connects to the Pinecone MCP server endpoint - Agent from Pydantic AI lets you define and run the AI assistant ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() ``` ### 5. Create a Tool Router Session What's happening: - We're creating a Tool Router session that gives your agent access to Pinecone tools - The create method takes the user ID and specifies which toolkits should be available - The returned session.mcp.url is the MCP server URL that your agent will use ```python async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Pinecone composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["pinecone"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") ``` ### 6. Initialize the Pydantic AI Agent What's happening: - The MCP client connects to the Pinecone endpoint - The agent uses GPT-5 to interpret user commands and perform Pinecone operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent pinecone_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[pinecone_mcp], instructions=( "You are a Pinecone assistant. Use Pinecone tools to help users " "with their requests. Ask clarifying questions when needed." ), ) ``` ### 7. Build the chat interface What's happening: - The agent reads input from the terminal and streams its response - Pinecone API calls happen automatically under the hood - The model keeps conversation history to maintain context across turns ```python # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Pinecone.\n") while True: user_input = input("You: ").strip() if user_input.lower() in {"exit", "quit", "bye"}: print("\nGoodbye!") break if not user_input: continue print("\nAgent is thinking...\n", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() ``` ### 8. Run the application What's happening: - The asyncio loop launches the agent and keeps it running until you exit ```python if __name__ == "__main__": asyncio.run(main()) ``` ## Complete Code ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Pinecone composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["pinecone"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") # Attach the MCP server to a Pydantic AI Agent pinecone_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[pinecone_mcp], instructions=( "You are a Pinecone assistant. Use Pinecone tools to help users " "with their requests. Ask clarifying questions when needed." ), ) # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Pinecone.\n") while True: user_input = input("You: ").strip() if user_input.lower() in {"exit", "quit", "bye"}: print("\nGoodbye!") break if not user_input: continue print("\nAgent is thinking...\n", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() if __name__ == "__main__": asyncio.run(main()) ``` ## Conclusion You've built a Pydantic AI agent that can interact with Pinecone through Composio's Tool Router. With this setup, your agent can perform real Pinecone actions through natural language. You can extend this further by: - Adding other toolkits like Gmail, HubSpot, or Salesforce - Building a web-based chat interface around this agent - Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Pinecone for workflow automation) This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations. ## How to build Pinecone MCP Agent with another framework - [ChatGPT](https://composio.dev/toolkits/pinecone/framework/chatgpt) - [Antigravity](https://composio.dev/toolkits/pinecone/framework/antigravity) - [OpenAI Agents SDK](https://composio.dev/toolkits/pinecone/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/pinecone/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/pinecone/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/pinecone/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/pinecone/framework/codex) - [Cursor](https://composio.dev/toolkits/pinecone/framework/cursor) - [VS Code](https://composio.dev/toolkits/pinecone/framework/vscode) - [OpenCode](https://composio.dev/toolkits/pinecone/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/pinecone/framework/openclaw) - [Hermes](https://composio.dev/toolkits/pinecone/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/pinecone/framework/cli) - [Google ADK](https://composio.dev/toolkits/pinecone/framework/google-adk) - [LangChain](https://composio.dev/toolkits/pinecone/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/pinecone/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/pinecone/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/pinecone/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/pinecone/framework/crew-ai) ## Related Toolkits - [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure. - [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed. - [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms. - [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place. - [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service. - [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. It's the go-to tool for calculations, reporting, and flexible data management. - [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows. - [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow. - [21risk](https://composio.dev/toolkits/_21risk) - 21RISK is a web app built for easy checklist, audit, and compliance management. It streamlines risk processes so teams can focus on what matters. - [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences. - [Abstract](https://composio.dev/toolkits/abstract) - Abstract provides a suite of APIs for automating data validation and enrichment tasks. It helps developers streamline workflows and ensure data quality with minimal effort. - [Abuselpdb](https://composio.dev/toolkits/abuselpdb) - Abuselpdb is a central database for reporting and checking IPs linked to malicious online activity. Use it to quickly identify and report suspicious or abusive IP addresses. - [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time. - [Agentql](https://composio.dev/toolkits/agentql) - Agentql is a toolkit that connects AI agents to the web using a specialized query language. It enables structured web interaction and data extraction for smarter automations. - [Agenty](https://composio.dev/toolkits/agenty) - Agenty is a web scraping and automation platform for extracting data and automating browser tasks—no coding needed. It streamlines data collection, monitoring, and repetitive online actions. - [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows. - [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application. - [Alchemy](https://composio.dev/toolkits/alchemy) - Alchemy is a blockchain development platform offering APIs and tools for Ethereum apps. It simplifies building and scaling Web3 projects with robust infrastructure. - [Algolia](https://composio.dev/toolkits/algolia) - Algolia is a hosted search API that powers lightning-fast, relevant search experiences for web and mobile apps. It helps developers deliver instant, typo-tolerant, and scalable search without complex infrastructure. - [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Pinecone MCP? With a standalone Pinecone MCP server, the agents and LLMs can only access a fixed set of Pinecone tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Pinecone and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with Pydantic AI? Yes, you can. Pydantic AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Pinecone tools. ### Can I manage the permissions and scopes for Pinecone while using Tool Router? Yes, absolutely. You can configure which Pinecone scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do. ### How safe is my data with Composio Tool Router? All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Pinecone data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)