iflow-mcp_mcp-pinecone 0.1.8
Read and write to Pinecone from Claude Desktop with Model Context Protocol.
Requires Python: >=3.12

# Pinecone Model Context Protocol Server for Claude Desktop.

[![smithery badge](https://smithery.ai/badge/mcp-pinecone)](https://smithery.ai/server/mcp-pinecone)

[![PyPI - Downloads](https://img.shields.io/pypi/dd/mcp-pinecone?style=flat)](https://pypi.org/project/mcp-pinecone/)

Read and write to a Pinecone index.


## Components

```mermaid
flowchart TB
    subgraph Client["MCP Client (e.g., Claude Desktop)"]
        UI[User Interface]
    end

    subgraph MCPServer["MCP Server (pinecone-mcp)"]
        Server[Server Class]
        
        subgraph Handlers["Request Handlers"]
            ListRes[list_resources]
            ReadRes[read_resource]
            ListTools[list_tools]
            CallTool[call_tool]
            GetPrompt[get_prompt]
            ListPrompts[list_prompts]
        end
        
        subgraph Tools["Implemented Tools"]
            SemSearch[semantic-search]
            ReadDoc[read-document]
            ListDocs[list-documents]
            PineconeStats[pinecone-stats]
            ProcessDoc[process-document]
        end
    end

    subgraph PineconeService["Pinecone Service"]
        PC[Pinecone Client]
        subgraph PineconeFunctions["Pinecone Operations"]
            Search[search_records]
            Upsert[upsert_records]
            Fetch[fetch_records]
            List[list_records]
            Embed[generate_embeddings]
        end
        Index[(Pinecone Index)]
    end

    %% Connections
    UI --> Server
    Server --> Handlers
    
    ListTools --> Tools
    CallTool --> Tools
    
    Tools --> PC
    PC --> PineconeFunctions
    PineconeFunctions --> Index
    
    %% Data flow for semantic search
    SemSearch --> Search
    Search --> Embed
    Embed --> Index
    
    %% Data flow for document operations
    UpsertDoc --> Upsert
    ReadDoc --> Fetch
    ListRes --> List

    classDef primary fill:#2563eb,stroke:#1d4ed8,color:white
    classDef secondary fill:#4b5563,stroke:#374151,color:white
    classDef storage fill:#059669,stroke:#047857,color:white
    
    class Server,PC primary
    class Tools,Handlers secondary
    class Index storage
```

### Resources

The server implements the ability to read and write to a Pinecone index.

### Tools

- `semantic-search`: Search for records in the Pinecone index.
- `read-document`: Read a document from the Pinecone index.
- `list-documents`: List all documents in the Pinecone index.
- `pinecone-stats`: Get stats about the Pinecone index, including the number of records, dimensions, and namespaces.
- `process-document`: Process a document into chunks and upsert them into the Pinecone index. This performs the overall steps of chunking, embedding, and upserting.

Note: embeddings are generated via Pinecone's inference API and chunking is done with a token-based chunker. Written by copying a lot from langchain and debugging with Claude.
## Quickstart

### Installing via Smithery

To install Pinecone MCP Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/mcp-pinecone):

```bash
npx -y @smithery/cli install mcp-pinecone --client claude
```

### Install the server

Recommend using [uv](https://docs.astral.sh/uv/getting-started/installation/) to install the server locally for Claude.

```
uvx install mcp-pinecone
```
OR
```
uv pip install mcp-pinecone
```

Add your config as described below.

#### Claude Desktop

On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json`
On Windows: `%APPDATA%/Claude/claude_desktop_config.json`

Note: You might need to use the direct path to `uv`. Use `which uv` to find the path.


__Development/Unpublished Servers Configuration__
  
```json
"mcpServers": {
  "mcp-pinecone": {
    "command": "uv",
    "args": [
      "--directory",
      "{project_dir}",
      "run",
      "mcp-pinecone"
    ]
  }
}
```


__Published Servers Configuration__
  
```json
"mcpServers": {
  "mcp-pinecone": {
    "command": "uvx",
    "args": [
      "--index-name",
      "{your-index-name}",
      "--api-key",
      "{your-secret-api-key}",
      "mcp-pinecone"
    ]
  }
}
```

#### Sign up to Pinecone

You can sign up for a Pinecone account [here](https://www.pinecone.io/).

#### Get an API key

Create a new index in Pinecone, replacing `{your-index-name}` and get an API key from the Pinecone dashboard, replacing `{your-secret-api-key}` in the config.

## Development

### Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:
```bash
uv sync
```

2. Build package distributions:
```bash
uv build
```

This will create source and wheel distributions in the `dist/` directory.

3. Publish to PyPI:
```bash
uv publish
```

Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token: `--token` or `UV_PUBLISH_TOKEN`
- Or username/password: `--username`/`UV_PUBLISH_USERNAME` and `--password`/`UV_PUBLISH_PASSWORD`

### Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging
experience, we strongly recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector).


You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command:

```bash
npx @modelcontextprotocol/inspector uv --directory {project_dir} run mcp-pinecone
```


Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Source Code

The source code is available on [GitHub](https://github.com/sirmews/mcp-pinecone).

## Contributing

Send your ideas and feedback to me on [Bluesky](https://bsky.app/profile/perfectlycromulent.bsky.social) or by opening an issue.