LanceDB vs Pinecone MCP Server: both are coding tools on Scriptiii AI Tools. LanceDB pricing: Free. Free to use. (reported by the maker) Pinecone MCP Server pricing: Free. Free to use. (reported by the maker) Both have 0 upvotes.
| Field | LanceDB | Pinecone MCP Server |
|---|---|---|
| Page | LanceDB | Pinecone MCP Server |
| Pricing (maker-reported) | Free. Free to use. | Free. Free to use. |
| Upvotes | 0 | 0 |
| Best for | ai-agents, tool, vector-databases | ai-agents, mcp-server, vector-databases |
| Tagline | Open-source embedded retrieval library and database for multimodal AI data. | Connects Pinecone projects and documentation search to Cursor, Claude and other MCP clients. |
| Website | Visit LanceDB | Visit Pinecone MCP Server |
Pricing and pricing details come from the maker's submission. They are not independently verified.
How to Install ✦ Detailed Documentation ✦ Tutorials and Recipes ✦ Contributors
The ultimate multimodal data platform for AI/ML applications.
LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease.
LanceDB is a central location where developers can build, train and analyze their AI workloads.
Follow the Quickstart doc to set up LanceDB locally.
API & SDK: We also support Python, Typescript and Rust SDKs
| Interface | Documentation |
|---|---|
| Python SDK | https://lancedb.github.io/lancedb/python/python/ |
| Typescript SDK | https://lancedb.github.io/lancedb/js/globals/ |
| Rust SDK | https://docs.rs/lancedb/latest/lancedb/index.html |
| REST API | https://docs.lancedb.com/api-reference/rest |
We welcome contributions from everyone! Whether you're a developer, researcher, or just someone who wants to help out.
If you have any suggestions or feature requests, please feel free to open an issue on GitHub or discuss it on our Discord server.
Check out the GitHub Issues if you would like to work on the features that are planned for the future. If you have any suggestions or feature requests, please feel free to open an issue on GitHub.
The Model Context Protocol (MCP) is a standard that allows coding assistants and other AI tools to interact with platforms like Pinecone. The Pinecone Developer MCP Server allows you to connect these tools with Pinecone projects and documentation.
Once connected, AI tools can:
See the docs for more detailed information.
This MCP server is focused on improving the experience of developers working with Pinecone as part of their technology stack. It is intended for use with coding assistants. Pinecone also offers the Assistant MCP, which is designed to provide AI assistants with relevant context sourced from your knowledge base.
To configure the MCP server to access your Pinecone project, you will need to generate an API key using the console. Without an API key, your AI tool will still be able to search documentation. However, it will not be able to manage or query your indexes.
The MCP server requires Node.js v22 or later. Ensure that node and npx are available in your PATH.
Next, you will need to configure your AI assistant to use the MCP server.
To add the Pinecone MCP server to a project, create a .cursor/mcp.json file in the project root (if it doesn't already exist) and add the following configuration:
{
"mcpServers": {
"pinecone": {
"command": "npx",
"args": [
"-y", "@pinecone-database/mcp"
],
"env": {
"PINECONE_API_KEY": "<your pinecone api key>"
}
}
}
}
You can check the status of the server in Cursor Settings > MCP.
To enable the server globally, add the configuration to the .cursor/mcp.json in your home directory instead.
It is recommended to use rules to instruct Cursor on proper usage of the MCP server. Check out the docs for some suggestions.
Use Claude desktop to locate the claude_desktop_config.json file by navigating to Settings > Developer > Edit Config. Add the following configuration:
{
"mcpServers": {
"pinecone": {
"command": "npx",
"args": [
"-y", "@pinecone-database/mcp"
],
"env": {
"PINECONE_API_KEY": "<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.
To install this as a Gemini CLI extension, run the following command:
gemini extensions install https://github.com/pinecone-io/pinecone-mcp
You will need to provide your Pinecone API key in the PINECONE_API_KEY environment variable.
export PINECONE_API_KEY=<your pinecone api key>
When you run gemini and press ctrl+t, pinecone should now be shown in the list of installed MCP servers.
Once configured, your AI tool will automatically make use of the MCP to interact with Pinecone. You may be prompted for permission before a tool can be used.
Here are some prompts you can try with your AI assistant:
Pinecone Developer MCP Server provides the following tools for AI assistants to use:
search-docs: Search the official Pinecone documentation.list-indexes: Lists all Pinecone indexes.describe-index: Describes the configuration of an index.describe-index-stats: Provides statistics about the data in the index, including the number of records and available namespaces.create-document-index: Creates a document index with searchable string fields, each indexed for full-text search, embedded by a hosted model (integrated embedding), or both.upsert-documents: Inserts or updates documents in a document index.search-document-index: Searches a document index by meaning (embed, sparse_embed) or by keywords (text, query_string), with optional filters.create-index-for-model: Creates a legacy integrated index that uses an integrated inference model to embed text as vectors.upsert-records: Inserts or updates records in an index with integrated inference.search-records: Searches for records in an index based on a text query, using integrated inference for embedding. Has options for metadata filtering and reranking.cascading-search: Searches for records across multiple indexes, deduplicating and reranking the results.rerank-documents: Reranks a collection of records or text documents using a specialized reranking model.Document indexes with string fields (full-text search and integrated embedding) and legacy integrated indexes are supported. Assistants, standalone embeddings, and search with your own vectors are not supported.
node --versionnpx is available in your PATH: which npxPINECONE_API_KEY environment variable is set correctly in your MCP configurationapi.pinecone.ioPINECONE_API_KEY=<your-key> npx @pinecone-database/mcpWe welcome your collaboration in improving the developer MCP experience. Please submit issues in the GitHub issue tracker. Information about contributing can be found in CONTRIBUTING.md.