pinecone-io/assistant-mcp

Overview

The Pinecone Assistant MCP server is an open-source Rust implementation of the Model Context Protocol for Pinecone Assistant. It enables MCP-compatible AI clients, such as Claude Desktop, to query and interact with Pinecone indexes through a standardized interface.

Best for

Best for
Developers who need a performant, standards-based bridge between AI assistants and Pinecone vector databases

Use cases

• Connect AI assistants to Pinecone vector databases for retrieval-augmented generation
• Enable semantic search features in MCP-enabled tools and agents
• Build knowledge base integrations that leverage Pinecone’s indexing and querying capabilities

How to use

Install

cargo build --release

Tools exposed

• docker
• cargo
• npx

Tested with

Claude Desktop

Example client config

{\n  "mcpServers": {\n    "pinecone-assistant": {\n      "command": "docker",\n      "args": [\n        "run", \n        "-i", \n        "--rm", \n        "-e", \n        "PINECONE_API_KEY", \n        "-e", \n        "PINECONE_ASSISTANT_HOST", \n        "pinecone/assistant-mcp"\n      ],\n      "env": {\n        "PINECONE_API_KEY": "<YOUR_PINECONE_API_KEY_HERE>",\n        "PINECONE_ASSISTANT_HOST": "<YOUR_PINECONE_ASSISTANT_HOST_HERE>"\n      }\n    }\n  }\n}

Notes

The Pinecone Assistant MCP server is an open-source Rust implementation of the Model Context Protocol for Pinecone Assistant. It enables MCP-compatible AI clients, such as Claude Desktop, to query and interact with Pinecone indexes through a standardized interface.

43 stars on GitHub. Last updated 2025-04-17. Licensed MIT.

Use cases

• Connect AI assistants to Pinecone vector databases for retrieval-augmented generation
• Enable semantic search features in MCP-enabled tools and agents
• Build knowledge base integrations that leverage Pinecone’s indexing and querying capabilities

Pros

• Rust-based for performance and low resource consumption
• Adheres to the MCP standard, promoting interoperability with multiple clients
• Lightweight and easy to deploy for users already familiar with Pinecone

Cons

• Low community adoption (43 stars) signals limited real-world testing and support
• Early-stage project with sparse documentation and potential stability issues
• Requires familiarity with both Pinecone Assistant and the MCP protocol to use effectively

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.