Pinecone MCP Server Integration

What does “Pinecone” MCP Server do?

The Pinecone MCP (Model Context Protocol) Server is a specialized tool that connects AI assistants with Pinecone vector databases, enabling seamless reading and writing of data for enhanced development workflows. By serving as an intermediary, the Pinecone MCP Server allows AI clients to execute tasks such as semantic search, document retrieval, and database management within a Pinecone index. It supports operations like querying for similar records, managing documents, and upserting new embeddings. This capability is particularly valuable for applications involving Retrieval-Augmented Generation (RAG), as it streamlines the integration of contextual data into AI workflows and automates complex data interactions.

List of Prompts

No explicit prompt templates are mentioned in the repository.

List of Resources

• Pinecone Index: The primary resource, allowing for reading and writing data.
• Document Resource: Represents documents stored within the Pinecone index that can be read or listed.
• Record Resource: Individual records within the Pinecone index which can be searched or upserted.
• Pinecone Stats Resource: Exposes statistics about the Pinecone index, such as record counts, dimensions, and namespaces.

List of Tools

• semantic-search: Searches for records in the Pinecone index using semantic similarity.
• read-document: Reads a specific document from the Pinecone index.
• list-documents: Lists all documents currently stored in the Pinecone index.
• pinecone-stats: Retrieves statistics about the Pinecone index, including the number of records, their dimensions, and namespaces.
• process-document: Processes a document into chunks, generates embeddings, and upserts them into the Pinecone index.

Use Cases of this MCP Server

• Database Management: Efficiently read, write, and manage vector data within a Pinecone index, supporting large-scale AI applications.
• Semantic Search: Enable AI assistants to perform semantic searches over stored documents, returning the most relevant matches based on vector similarity.
• Retrieval-Augmented Generation (RAG): Integrate external knowledge into LLM workflows by retrieving relevant context from the Pinecone index to inform AI responses.
• Document Chunking and Embedding: Automatically chunk documents, generate embeddings, and insert them into Pinecone, streamlining the workflow for document search and retrieval.
• Index Monitoring and Statistics: Obtain real-time insights into the Pinecone index’s health and performance, aiding in optimization and troubleshooting.

How to set it up

Windsurf

1. Ensure you have Python and Node.js installed.
2. Locate your Windsurf configuration file.
3. Add the Pinecone MCP Server using the following JSON snippet:

   {
     "mcpServers": {
       "pinecone-mcp": {
         "command": "mcp-pinecone",
         "args": []
       }
     }
   }
   

4. Save the configuration file and restart Windsurf.
5. Verify by checking for Pinecone MCP Server tools in the interface.

Securing API keys with environment variables:

{
  "mcpServers": {
    "pinecone-mcp": {
      "command": "mcp-pinecone",
      "env": {
        "PINECONE_API_KEY": "your_api_key"
      },
      "inputs": {
        "index_name": "your_index"
      }
    }
  }
}

Claude

1. Install the Pinecone MCP Server using Python (e.g., pip install mcp-pinecone).
2. Edit your Claude configuration to add the server:

   {
     "mcpServers": {
       "pinecone-mcp": {
         "command": "mcp-pinecone",
         "args": []
       }
     }
   }
   

3. Save the configuration and restart Claude.
4. Confirm the server is running and accessible as a tool.

Cursor

1. Make sure Python and mcp-pinecone are installed.
2. Go to your Cursor configuration file.
3. Insert the following MCP server entry:

   {
     "mcpServers": {
       "pinecone-mcp": {
         "command": "mcp-pinecone",
         "args": []
       }
     }
   }
   

4. Save changes and restart Cursor.
5. Check the tool list for Pinecone operations.

Cline

1. Verify Python and mcp-pinecone installation.
2. Open Cline’s configuration file.
3. Add the Pinecone MCP Server with:

   {
     "mcpServers": {
       "pinecone-mcp": {
         "command": "mcp-pinecone",
         "args": []
       }
     }
   }
   

4. Save and restart Cline.
5. Ensure you can access Pinecone tools.

Note: Always secure API keys and sensitive values with environment variables as shown above.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "pinecone-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “pinecone-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

The Pinecone MCP Server is well-documented, exposes clear resources and tools, and includes solid instructions for integration and API key security. However, it lacks explicit prompt templates and documentation on sampling or roots support. Overall, it is a practical and valuable server for RAG and Pinecone workflows, though it could be improved with more workflow examples and advanced features.

Rating: 8/10

MCP Score