Pinecone Developer MCP Server

Pinecone Developer MCP Server

What is Pinecone Developer MCP Server about?

• Provides a Model Context Protocol (MCP) server that bridges coding assistants (Cursor, Claude, Gemini CLI, etc.) with Pinecone services.
• Allows assistants to query documentation, list and describe indexes, create inference‑enabled indexes, upsert records, and run searches.

How to use Pinecone Developer MCP Server?

1. Install Node.js and ensure node and npx are in your PATH.
2. Configure the MCP server for your AI tool (e.g., Cursor, Claude desktop, Gemini CLI) by adding a .cursor/mcp.json or equivalent configuration that runs npx -y @pinecone-database/mcp with your Pinecone API key.
3. Start the AI assistant; it will automatically connect to the MCP server and expose tools such as search-docs, list-indexes, create-index-for-model, etc.
4. Interact via natural language – ask the assistant to create an index, upload data, or perform a search, and the server will invoke the appropriate Pinecone API calls.

Key features of Pinecone Developer MCP Server

• Documentation search – search-docs tool queries Pinecone's official docs.
• Index management – list, describe, and create inference‑enabled indexes.
• Data operations – upsert records with integrated inference and perform text‑based searches.
• Advanced search – cascading search across multiple indexes, reranking, and document reranking.
• Cross‑assistant support – works with Cursor, Claude desktop, Gemini CLI, and any MCP‑compatible tool.

Use cases of Pinecone Developer MCP Server

• Rapid prototyping – developers can ask an AI assistant to spin up a vector index and ingest sample data in seconds.
• On‑demand troubleshooting – AI can fetch relevant Pinecone documentation to resolve configuration issues.
• Automated testing – scripts driven by an assistant can upsert test vectors and verify search results.
• Knowledge‑augmented coding – while writing code, assistants can retrieve index schemas or stats without leaving the editor.

FAQ from the Pinecone Developer MCP Server

• Do I need a Pinecone API key? Yes, for any operation that manages or queries your indexes. Without a key, the assistant can only search documentation.
• What runtimes are supported? The server runs on Node.js; npx is used to invoke the package.
• Which indexes are supported? Only indexes with integrated inference (vector creation via Pinecone's built‑in model) are available.
• Can I use the server globally? Place the MCP configuration file in your home directory’s .cursor folder to make it available across all projects.
• Is the server open‑source? Yes, the source code is hosted on GitHub at https://github.com/pinecone-io/pinecone-mcp.

Pinecone Developer MCP Server

The Model Context Protocol (MCP) is a standard that allows coding assistants and other AI tools to interact with platforms like Pinecone. The Pinecone Developer MCP Server allows you to connect these tools with Pinecone projects and documentation.

Once connected, AI tools can:

• Search Pinecone documentation to answer questions accurately.
• Help you configure indexes based on your application's needs.
• Generate code informed by your index configuration and data, as well as Pinecone documentation and examples.
• Upsert and search for data in indexes, allowing you to test queries and evaluate results within your dev environment.

See the docs for more detailed information.

This MCP server is focused on improving the experience of developers working with Pinecone as part of their technology stack. It is intended for use with coding assistants. Pinecone also offers the Assistant MCP, which is designed to provide AI assistants with relevant context sourced from your knowledge base.

Setup

To configure the MCP server to access your Pinecone project, you will need to generate an API key using the console. Without an API key, your AI tool will still be able to search documentation. However, it will not be able to manage or query your indexes.

The MCP server requires Node.js. Ensure that node and npx are available in your PATH.

Next, you will need to configure your AI assistant to use the MCP server.

Configure Cursor

To add the Pinecone MCP server to a project, create a .cursor/mcp.json file in the project root (if it doesn't already exist) and add the following configuration:

{
  "mcpServers": {
    "pinecone": {
      "command": "npx",
      "args": [
        "-y", "@pinecone-database/mcp"
      ],
      "env": {
        "PINECONE_API_KEY": "<your pinecone api key>"
      }
    }
  }
}

You can check the status of the server in Cursor Settings > MCP.

To enable the server globally, add the configuration to the .cursor/mcp.json in your home directory instead.

It is recommended to use rules to instruct Cursor on proper usage of the MCP server. Check out the docs for some suggestions.

Configure Claude desktop

Use Claude desktop to locate the claude_desktop_config.json file by navigating to Settings > Developer > Edit Config. Add the following configuration:

{
  "mcpServers": {
    "pinecone": {
      "command": "npx",
      "args": [
        "-y", "@pinecone-database/mcp"
      ],
      "env": {
        "PINECONE_API_KEY": "<your pinecone api key>"
      }
    }
  }
}

Restart Claude desktop. On the new chat screen, you should see a hammer (MCP) icon appear with the new MCP tools available.

Use as a Gemini CLI extension

To install this as a Gemini CLI extension, run the following command:

gemini extensions install https://github.com/pinecone-io/pinecone-mcp

You will need to provide your Pinecone API key in the PINECONE_API_KEY environment variable.

export PINECONE_API_KEY=<your pinecone api key>

When you run gemini and press ctrl+t, pinecone should now be shown in the list of installed MCP servers.

Usage

Once configured, your AI tool will automatically make use of the MCP to interact with Pinecone. You may be prompted for permission before a tool can be used. Try asking your AI assistant to set up an example index, upload sample data, or search for you!

Tools

Pinecone Developer MCP Server provides the following tools for AI assistants to use:

• search-docs: Search the official Pinecone documentation.
• list-indexes: Lists all Pinecone indexes.
• describe-index: Describes the configuration of an index.
• describe-index-stats: Provides statistics about the data in the index, including the number of records and available namespaces.
• create-index-for-model: Creates a new index that uses an integrated inference model to embed text as vectors.
• upsert-records: Inserts or updates records in an index with integrated inference.
• search-records: Searches for records in an index based on a text query, using integrated inference for embedding. Has options for metadata filtering and reranking.
• cascading-search: Searches for records across multiple indexes, deduplicating and reranking the results.
• rerank-documents: Reranks a collection of records or text documents using a specialized reranking model.

Limitations

Only indexes with integrated inference are supported. Assistants, indexes without integrated inference, standalone embeddings, and vector search are not supported.

Contributing

We welcome your collaboration in improving the developer MCP experience. Please submit issues in the GitHub issue tracker. Information about contributing can be found in CONTRIBUTING.md.