Pinecone MCP
UnclaimedConnect Pinecone projects to AI assistants for enhanced development workflows.
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TL;DR - Pinecone MCP
- Connects Pinecone projects and documentation to AI coding assistants.
- Enables AI to search docs, configure indexes, and generate Pinecone-specific code.
- Facilitates data upsertion and searching within indexes directly from AI tools.
Pricing: Free forever
Best for: Individuals & startups
Pros & Cons
Pros
- Streamlines Pinecone development by integrating AI assistance directly into workflows
- Enhances accuracy of AI responses by providing direct access to Pinecone documentation
- Automates index configuration and data management tasks through AI commands
- Supports testing and evaluation of Pinecone queries within the development environment
- Compatible with popular AI coding assistants like Cursor, Claude, and Gemini
Cons
- Requires Node.js v18 or later for server setup
- An API key is necessary for AI tools to manage or query indexes, otherwise only documentation search is available
- Configuration involves manual file editing for some integrations
Preview
Key Features
Search official Pinecone documentation via AI assistantsList all Pinecone indexesDescribe index configurationsProvide index statistics (record count, namespaces)Create new indexes with integrated inference modelsUpsert and update records in indexes with integrated inferenceSearch for records in indexes using text queries and integrated inferenceSupport for metadata filtering and reranking during record searches
Pricing Plans
Open Source
Free
- Full source code access
- Community support
- Self-hosted
About Pinecone MCP
By Toolradar Team·
The Pinecone Developer MCP Server is a standard that enables coding assistants and other AI tools to interact directly with Pinecone projects and documentation. It allows AI tools to accurately search Pinecone documentation, configure indexes based on application needs, and generate code informed by index configurations and data. Developers can also use it to upsert and search data within indexes, facilitating testing and evaluation directly within their development environment.
This server is specifically designed to improve the experience for developers utilizing Pinecone within their technology stack, primarily through integration with coding assistants. It provides a robust set of tools for managing and querying Pinecone indexes, making it easier to leverage vector databases in AI-powered applications. For broader AI assistant context sourcing from general knowledge bases, Pinecone offers a separate Assistant MCP.
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Pinecone MCP FAQ
What is the difference between the Pinecone Developer MCP Server and the Pinecone Assistant MCP?
The Pinecone Developer MCP Server is specifically tailored for developers working with Pinecone, enabling coding assistants to interact with Pinecone projects and documentation for tasks like index configuration, code generation, and data management. The Pinecone Assistant MCP, on the other hand, is designed to provide AI assistants with relevant context sourced from a broader knowledge base, not exclusively Pinecone-specific development tasks.
What capabilities are restricted if I don't provide a Pinecone API key during setup?
Without a Pinecone API key, your AI tool will still be able to search the official Pinecone documentation to answer questions. However, it will not be able to perform actions that involve managing or querying your Pinecone indexes, such as listing indexes, creating new ones, upserting data, or searching records.
Can I use this MCP server with AI assistants other than Cursor, Claude, or Gemini?
The documentation explicitly details configuration steps for Cursor, Claude desktop, and as a Gemini CLI extension. While the Model Context Protocol (MCP) is a standard, compatibility with other AI assistants would depend on their support for custom MCP server integrations, which may require additional configuration not covered here.
How does the `create-index-for-model` tool function, and what are its requirements?
The
create-index-for-model tool allows AI assistants to create a new Pinecone index. This index is specifically configured to use an integrated inference model, which means it can automatically embed text as vectors. To use this tool, you would typically provide the desired index name and specify the model to be used, such as 'multilingual-e5-large' as shown in the example prompts.What kind of information can the `describe-index-stats` tool provide to an AI assistant?
The
describe-index-stats tool offers an AI assistant statistics about the data contained within a specified Pinecone index. This includes crucial information such as the total number of records stored in the index and a breakdown of the namespaces that exist within it, giving a clear overview of the index's content and structure.Source: github.com