Pinecone
Model Context Protocol server to allow for reading and writing from Pinecone. Rudimentary RAG
What is Pinecone?
Pinecone is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to model context protocol server to allow for reading and writing from pinecone. rudimentary rag
Model Context Protocol server to allow for reading and writing from Pinecone. Rudimentary RAG
This server falls under the Data Science & ML category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Model Context Protocol server to allow for reading and writi
Use Cases
Maintainer
Works with
Installation
NPM
npx -y mcp-pineconeManual Installation
npx -y mcp-pineconeConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Pinecone
The mcp-pinecone server bridges AI assistants with Pinecone, the managed vector database, enabling semantic search, document ingestion, and retrieval-augmented generation (RAG) workflows through natural language. It connects to your Pinecone index and exposes tools to process documents into embedding chunks, upsert them into the index, list or read stored documents, retrieve index statistics, and run semantic similarity searches — all without writing API code. Developers building RAG pipelines, knowledge bases, or semantic search features use it to let Claude interact directly with their Pinecone data as part of a conversational workflow.
Prerequisites
- Node.js 18+ or a Python environment with uv/uvx available
- A Pinecone account with at least one index created (https://www.pinecone.io/)
- Pinecone API key from the Pinecone dashboard
- Pinecone index name that has been configured with the correct dimensions
- An MCP client such as Claude Desktop or Claude Code
Create a Pinecone index
Log in to https://app.pinecone.io, create a new index, and note its name. The index dimensions must match the embedding model the server uses (Pinecone's inference API handles this automatically when using the server's process-document tool).
Obtain your Pinecone API key
In the Pinecone console, navigate to API Keys and copy your secret key. Keep it secure — you will pass it to the server as a configuration argument.
Install the server via uvx
The recommended way to run mcp-pinecone is with uvx, which installs and runs the package in an isolated environment without polluting your global Python installation.
uvx mcp-pineconeAdd to your MCP client configuration
Configure your MCP client to launch mcp-pinecone with your index name and API key as command-line arguments. Replace the placeholder values with your real credentials.
{
"mcpServers": {
"mcp-pinecone": {
"command": "uvx",
"args": ["--index-name", "your-index-name", "--api-key", "your-secret-api-key", "mcp-pinecone"]
}
}
}Verify the connection
After restarting your MCP client, ask Claude to retrieve index statistics to confirm the server is connected and the index is accessible.
Pinecone Examples
Client configuration
Example claude_desktop_config.json entry for the Pinecone MCP server using uvx.
{
"mcpServers": {
"mcp-pinecone": {
"command": "uvx",
"args": ["--index-name", "your-index-name", "--api-key", "your-secret-api-key", "mcp-pinecone"]
}
}
}Prompts to try
Natural language requests that use the server's five exposed tools.
- "Show me the stats for my Pinecone index — how many records are stored and what are the namespaces?"
- "Process and upsert the following document into the index: [paste document text here]"
- "Search the index for documents semantically similar to 'machine learning model deployment strategies'."
- "List all documents stored in the index."
- "Read the document with ID 'doc-0042' from the Pinecone index."Troubleshooting Pinecone
Authentication error: 'Unauthorized' or 'Invalid API key'
Double-check the --api-key argument matches exactly what is shown in the Pinecone dashboard under API Keys. API keys are sensitive and must be copied in full without extra spaces.
Index not found error when running semantic-search or list-documents
Verify the --index-name argument matches the exact index name in your Pinecone project (case-sensitive). The index must exist in the same Pinecone environment as your API key.
process-document tool fails with a dimension mismatch error
The index dimensions must be compatible with Pinecone's inference API embedding model. Create a new index using Pinecone's integrated inference (serverless), which auto-configures dimensions, rather than a fixed-dimension index.
Frequently Asked Questions about Pinecone
What is Pinecone?
Pinecone is a Model Context Protocol (MCP) server that model context protocol server to allow for reading and writing from pinecone. rudimentary rag It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Pinecone?
Install via npm with the command: npx -y mcp-pinecone. Then add the server configuration to your AI client's JSON config file (e.g., claude_desktop_config.json or .cursor/mcp.json).
Which AI clients work with Pinecone?
Pinecone works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Pinecone free to use?
Yes, Pinecone is open source and available under the MIT License license. You can use it freely in both personal and commercial projects.
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Quick Config Preview
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