mcp-pinecone

A Pinecone-backed Model Context Protocol server for semantic search and document management.

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mcp-pinecone implements a Model Context Protocol (MCP) server that integrates with Pinecone indexes for use with clients such as Claude Desktop. It provides powerful tools for semantic search, document reading, listing, and processing within a Pinecone vector database. The server supports operations like embedding, chunking, and upserting records, enabling contextual management of large document sets. Designed for ease of installation and interoperability via the MCP standard.

Key Features

Read and write operations on Pinecone indexes

Semantic search capabilities over stored records

Document reading and listing tools

Statistics reporting for Pinecone indexes

Chunking and embedding generation for documents

Standardized MCP request and tool handlers

Integration with Claude Desktop as client

Automated installation via Smithery and PyPI

Processing and upserting of document chunks

Support for Pinecone's inference API

Use Cases

Enabling semantic document search within Pinecone-powered apps

Automating document ingestion and indexing with chunking and embedding

Providing context management for AI assistants such as Claude Desktop

Listing and querying documents in a vector database environment

Retrieving and summarizing Pinecone index statistics

Supporting conversational AI systems with context-driven search

Embedding generation and record upsertion for large document sets

Integrating Pinecone vector storage with standardized AI protocols

Developing tools for AI agents to read, write, and process documents

Facilitating programmable AI workflows through MCP endpoints

README

Pinecone Model Context Protocol Server for Claude Desktop.

Read and write to a Pinecone index.

Components

mermaid

flowchart TB
    subgraph Client["MCP Client (e.g., Claude Desktop)"]
        UI[User Interface]
    end

    subgraph MCPServer["MCP Server (pinecone-mcp)"]
        Server[Server Class]
        
        subgraph Handlers["Request Handlers"]
            ListRes[list_resources]
            ReadRes[read_resource]
            ListTools[list_tools]
            CallTool[call_tool]
            GetPrompt[get_prompt]
            ListPrompts[list_prompts]
        end
        
        subgraph Tools["Implemented Tools"]
            SemSearch[semantic-search]
            ReadDoc[read-document]
            ListDocs[list-documents]
            PineconeStats[pinecone-stats]
            ProcessDoc[process-document]
        end
    end

    subgraph PineconeService["Pinecone Service"]
        PC[Pinecone Client]
        subgraph PineconeFunctions["Pinecone Operations"]
            Search[search_records]
            Upsert[upsert_records]
            Fetch[fetch_records]
            List[list_records]
            Embed[generate_embeddings]
        end
        Index[(Pinecone Index)]
    end

    %% Connections
    UI --> Server
    Server --> Handlers
    
    ListTools --> Tools
    CallTool --> Tools
    
    Tools --> PC
    PC --> PineconeFunctions
    PineconeFunctions --> Index
    
    %% Data flow for semantic search
    SemSearch --> Search
    Search --> Embed
    Embed --> Index
    
    %% Data flow for document operations
    UpsertDoc --> Upsert
    ReadDoc --> Fetch
    ListRes --> List

    classDef primary fill:#2563eb,stroke:#1d4ed8,color:white
    classDef secondary fill:#4b5563,stroke:#374151,color:white
    classDef storage fill:#059669,stroke:#047857,color:white
    
    class Server,PC primary
    class Tools,Handlers secondary
    class Index storage

Resources

The server implements the ability to read and write to a Pinecone index.

Tools

semantic-search: Search for records in the Pinecone index.
read-document: Read a document from the Pinecone index.
list-documents: List all documents in the Pinecone index.
pinecone-stats: Get stats about the Pinecone index, including the number of records, dimensions, and namespaces.
process-document: Process a document into chunks and upsert them into the Pinecone index. This performs the overall steps of chunking, embedding, and upserting.

Note: embeddings are generated via Pinecone's inference API and chunking is done with a token-based chunker. Written by copying a lot from langchain and debugging with Claude.

Quickstart

Installing via Smithery

To install Pinecone MCP Server for Claude Desktop automatically via Smithery:

bash

npx -y @smithery/cli install mcp-pinecone --client claude

Install the server

Recommend using uv to install the server locally for Claude.

uvx install mcp-pinecone

uv pip install mcp-pinecone

Add your config as described below.

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

Note: You might need to use the direct path to uv. Use which uv to find the path.

Development/Unpublished Servers Configuration

json

"mcpServers": {
  "mcp-pinecone": {
    "command": "uv",
    "args": [
      "--directory",
      "{project_dir}",
      "run",
      "mcp-pinecone"
    ]
  }
}

Published Servers Configuration

json

"mcpServers": {
  "mcp-pinecone": {
    "command": "uvx",
    "args": [
      "--index-name",
      "{your-index-name}",
      "--api-key",
      "{your-secret-api-key}",
      "mcp-pinecone"
    ]
  }
}

Sign up to Pinecone

You can sign up for a Pinecone account here.

Get an API key

Create a new index in Pinecone, replacing {your-index-name} and get an API key from the Pinecone dashboard, replacing {your-secret-api-key} in the config.

Development

Building and Publishing

To prepare the package for distribution:

Sync dependencies and update lockfile:

bash

uv sync

Build package distributions:

bash

uv build

This will create source and wheel distributions in the dist/ directory.

Publish to PyPI:

bash

uv publish

Note: You'll need to set PyPI credentials via environment variables or command flags:

Token: --token or UV_PUBLISH_TOKEN
Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

bash

npx @modelcontextprotocol/inspector uv --directory {project_dir} run mcp-pinecone

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Source Code

The source code is available on GitHub.

Contributing

Send your ideas and feedback to me on Bluesky or by opening an issue.

Star History

Repository Owner

sirmews

User

Repository Details

Language Python

Default Branch main

Size 115 KB

Contributors 2

License MIT License

MCP Verified Nov 12, 2025

Programming Languages

Python

93.01%

Makefile

4.38%

Dockerfile

2.61%

Topics

claude mcp mcp-server model-context-protocol pinecone rag

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mcp-pinecone

Key Features

Use Cases

README

Pinecone Model Context Protocol Server for Claude Desktop.

Components

Resources

Tools

Quickstart

Installing via Smithery

Install the server

Claude Desktop

Sign up to Pinecone

Get an API key

Development

Building and Publishing

Debugging

License

Source Code

Contributing

Star History

Repository Owner

Repository Details

Programming Languages

Tags

Topics

Related MCPs