Pinecone Developer MCP Server

What is Pinecone Developer MCP Server about?

Pinecone Developer MCP Server bridges Pinecone indexes with AI coding assistants such as Cursor and Claude, providing tools that let assistants search Pinecone documentation, list and describe indexes, create inference‑enabled indexes, upsert and search records, and perform cascading searches.

How to use Pinecone Developer MCP Server?

1. Install Node.js and ensure node and npx are in the PATH.
2. Generate a Pinecone API key from the Pinecone console.
3. Add the MCP server configuration to your AI assistant (e.g., create a .cursor/mcp.json or edit claude_desktop_config.json) with the npx command and API key.
4. Restart the assistant; the MCP icon appears, indicating the tools are ready.
5. Interact with the assistant using natural language prompts like “create an index for a BERT model” or “search the docs for upsert‑records”. The assistant will invoke the appropriate MCP tool after obtaining permission.

Key Features of Pinecone Developer MCP Server

• search-docs – query official Pinecone documentation.
• list-indexes, describe-index, describe-index-stats – retrieve index information and statistics.
• create-index-for-model – provision a new index that includes integrated inference for automatic vector embedding.
• upsert-records – add or update records with built‑in inference.
• search-records – perform text‑based queries with optional metadata filtering and reranking.
• cascading-search – query across multiple indexes with deduplication and reranking.
• rerank-documents – improve relevance using a specialized reranking model.

Use Cases of Pinecone Developer MCP Server

• Rapid prototyping: developers can ask the assistant to spin up an inference‑enabled index and immediately test data ingestion and search.
• Context‑aware coding: while writing code, the assistant can fetch exact Pinecone API usage examples from the docs.
• Data validation: upsert sample records and retrieve stats to verify index health without leaving the IDE.
• Cross‑index search: retrieve unified results from several related indexes for recommendation or retrieval‑augmented generation tasks.

FAQ

Q: Do I need an API key to use the server? A: An API key is required for any operation that manages or queries your Pinecone indexes. Without it, only documentation search works.

Q: Which assistants are supported? A: The server works with any MCP‑compatible tool, such as Cursor, Claude desktop, and other coding assistants that can invoke MCP servers.

Q: Are indexes without integrated inference supported? A: No. The server only operates on indexes that have integrated inference enabled.

Q: How do I globally enable the server? A: Place the same mcp.json configuration in the .cursor folder of your home directory.

Q: Can I customize the tool permissions? A: Assistants typically ask for permission before invoking a tool; you can grant or deny per request.

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.

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.