Potencia tu agente
El comando propio del publicador. No es un endpoint de PlugMCP.
Host a copy with our keys and logs.
npx -y @pinecone-database/[email protected]Claude Code no tiene enlace de instalación para un comando local. Copia el comando de abajo y ejecútalo en una terminal.
claude mcp add pinecone-database-mcp -- npx -y @pinecone-database/mcp@0.4.0Ejecútalo en una terminal.
{ "mcpServers": { "pinecone-database-mcp": { "command": "npx", "args": [ "-y", "@pinecone-database/[email protected]" ] } }}claude_desktop_config.json
¿URL remota o un comando en tu máquina? En qué se diferencian las dos instalaciones
Ejecutar en PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
AbrirDel publicador
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.
Model Context Protocol server for Pinecone - enables AI assistants to interact with Pinecone indexes 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 v20 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-index-for-model: Creates a new 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.Only indexes with integrated inference are supported. Assistants, indexes without integrated inference, standalone embeddings, and vector search 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.
Model Context Protocol server for Pinecone - enables AI assistants to interact with Pinecone indexes and documentation
{ "mcpServers": { "pinecone-database-mcp": { "command": "npx", "args": [ "-y", "@pinecone-database/[email protected]" ] } }}Herramientas
Aún no hay lista de herramientas. Los servidores remotos la reciben tras la primera comprobación de estado.
Preguntas frecuentes
Copia en esta página la configuración stdio para Cursor, Claude Desktop, Claude Code o VS Code. Usa la instalación del publicador, no un proxy de PlugMCP.
Este registro no indica ninguna variable de entorno obligatoria.
@pinecone Database/mcp se ejecuta localmente por stdio.
Última comprobación: 2026-11-13. Model Context Protocol server for Pinecone - enables AI assistants to interact with Pinecone indexes and documentation