Use the Pinecone MCP server

Install from the MCP Store

1. Open the MCP Store via the "..." dropdown at the top of the editor's agent panel.
2. Find Pinecone in the list of supported servers and click Install.
3. Follow the on-screen prompts to authenticate and set your Pinecone API key.

Add via raw config

1. Open the MCP Store via the "..." dropdown at the top of the editor's agent panel.
2. Click Manage MCP Servers, then View raw config.
3. Edit mcp_config.json and add the Pinecone server:

{
  "mcpServers": {
    "pinecone": {
      "command": "npx",
      "args": [
        "-y", "@pinecone-database/mcp"
      ],
      "env": {
        "PINECONE_API_KEY": "{{YOUR_API_KEY}}"
      }
    }
  }
}

Replace YOUR_API_KEY with your Pinecone API key.

After installing or saving the config, the Pinecone server and its tools should appear in the agent panel. Use the MCP tools list to confirm the server is connected.

In the agent chat, try prompts that use Pinecone. For example, try generating code that creates an index, upserts records, or searches the index. The AI can use the connected MCP server for context and actions.

Run the following command to add the Pinecone MCP server to your Claude Code instance:

claude mcp add-json pinecone-mcp \
  '{"type": "stdio",
    "command": "npx",
    "args": ["-y", "@pinecone-database/mcp"],
    "env": {"PINECONE_API_KEY": "YOUR_API_KEY"}}'

Restart Claude Code. Then, run the /mcp command to check the status of the Pinecone MCP. You should see the following:

  > /mcp 
    ⎿  MCP Server Status

      • pinecone-mcp: ✓ connected

Test the Pinecone MCP server with prompts to Claude Code that require the server to generate Pinceone-compatible code and perform tasks in your Pinecone account.

Generate code:

Write a Python script that creates an index for dense vectors with integrated embedding, upserts 20 sentences about dogs, waits 10 seconds, searches the index, and reranks the results.

Perform tasks:

Create an index for dense vectors with integrated embedding, upsert 20 sentences about dogs, waits 10 seconds, search the index, and reranks the results.

Go to Settings > Developer > Edit Config and add the following configuration:

{
  "mcpServers": {
    "pinecone": {
      "command": "npx",
      "args": [
        "-y", "@pinecone-database/mcp"
      ],
      "env": {
        "PINECONE_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

Replace YOUR_API_KEY with 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.

Test the Pinecone MCP server with prompts that required the server to generate Pinceone-compatible code and perform tasks in your Pinecone account.

Generate code:

Write a Python script that creates an index for dense vectors with integrated embedding, upserts 20 sentences about dogs, waits 10 seconds, searches the index, and reranks the results.

Perform tasks:

Create an index for dense vectors with integrated embedding, upsert 20 sentences about dogs, waits 10 seconds, search the index, and reranks the results.

In your project root, create a .cursor/mcp.json file, if it doesn't exist, and add the following configuration:

{
  "mcpServers": {
    "pinecone": {
      "command": "npx",
      "args": [
        "-y", "@pinecone-database/mcp"
      ],
      "env": {
        "PINECONE_API_KEY": "{{YOUR_API_KEY}}"
      }
    }
  }
}

Go to Cursor Settings > MCP. You should see the server and its list of tools.

The Pinecone MCP server works well out of the box. However, you can add explicit rules to ensure the server behaves as you expect.

In your project root, create a .cursor/rules/pinecone.mdc file and add the following:

### Tool Usage for Code Generation

- When generating code related to Pinecone, always use the `pinecone` MCP and the `search_docs` tool.

- Perform at least two distinct searches per request using different, relevant questions to ensure comprehensive context is gathered before writing code.

### Error Handling

- If an error occurs while executing Pinecone-related code, immediately invoke the `pinecone` MCP and the `search_docs` tool.

- Search for guidance on the specific error encountered and incorporate any relevant findings into your resolution strategy.

### Syntax and Version Accuracy

- Before writing any code, verify and use the correct syntax for the latest stable version of the Pinecone SDK.

- Prefer official code snippets and examples from documentation over generated or assumed field values.

- Do not fabricate field names, parameter values, or request formats.

### SDK Installation Best Practices

- When providing installation instructions, always reference the current official package name.

- For Pinecone, use `pip install pinecone` not deprecated packages like `pinecone-client`.

Press Command + i to open the Agent chat. Test the Pinecone MCP server with prompts that required the server to generate Pinceone-compatible code and perform tasks in your Pinecone account.

Generate code:

Write a Python script that creates an index for dense vectors with integrated embedding, upserts 20 sentences about dogs, waits 10 seconds, searches the index, and reranks the results.

Perform tasks:

Create an index for dense vectors with integrated embedding, upsert 20 sentences about dogs, waits 10 seconds, search the index, and reranks the results.