mcp-pinecone
Model Context Protocol server to allow for reading and writing from Pinecone. Rudimentary RAG Python-based implementation.
Documentation
Pinecone Model Context Protocol Server for Claude Desktop.
Read and write to a Pinecone index.
Components
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 storageResources
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:
npx -y @smithery/cli install mcp-pinecone --client claudeInstall the server
Recommend using uv to install the server locally for Claude.
uvx install mcp-pineconeOR
uv pip install mcp-pineconeAdd 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__
"mcpServers": {
"mcp-pinecone": {
"command": "uv",
"args": [
"--directory",
"{project_dir}",
"run",
"mcp-pinecone"
]
}
}__Published Servers Configuration__
"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.
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:
uv sync2. Build package distributions:
uv buildThis will create source and wheel distributions in the `dist/` directory.
3. Publish to PyPI:
uv publishNote: 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.
You can launch the MCP Inspector via `npm` with this command:
npx @modelcontextprotocol/inspector uv --directory {project_dir} run mcp-pineconeUpon 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 file for details.
Source Code
The source code is available on GitHub.
Contributing
Send your ideas and feedback to me on Bluesky or by opening an issue.
Frequently asked questions
What is mcp-pinecone?
mcp-pinecone is Model Context Protocol server to allow for reading and writing from Pinecone. Rudimentary RAG Python-based implementation.
How do I install mcp-pinecone?
Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.
Is mcp-pinecone open source?
Yes — it is hosted on GitHub at https://github.com/sirmews/mcp-pinecone and has 148 stars.
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