Pinecone MCP Server
The official Pinecone Developer MCP server connects AI agents to your Pinecone vector database. Agents can search the Pinecone docs, list and describe indexes and their stats, create integrated-inference indexes, upsert and search records, run cascading searches across indexes, and rerank documents — for retrieval-augmented generation.
Install Pinecone
- Published by
- Official (vendor)
- Transport
- Local process (stdio)
- Authentication
- API key
- Package
- @pinecone-database/mcp
Claude Desktop, Claude Code and Cursor
Add this to the mcpServers object in your client's
config file, then restart the client.
{
"mcpServers": {
"pinecone": {
"command": "npx",
"args": [
"-y",
"@pinecone-database/mcp"
],
"env": {
"PINECONE_API_KEY": "${PINECONE_API_KEY}"
}
}
}
}VS Code uses a different key — show that config
Identical entry, filed under servers rather than
mcpServers. Put it in
.vscode/mcp.json for one workspace.
{
"servers": {
"pinecone": {
"command": "npx",
"args": [
"-y",
"@pinecone-database/mcp"
],
"env": {
"PINECONE_API_KEY": "${PINECONE_API_KEY}"
}
}
}
}Before it will answer
One API key from the Pinecone console. Needs Node.js with node and npx on PATH — npx resolves against the PATH your MCP client inherits, not your shell's, which is why a config that works in a terminal can fail inside a desktop app.
Tools
cascading-search
Search across multiple indexes, deduplicating and reranking.
create-index-for-model
Create an index that embeds text using a hosted model.
describe-index
Describe the configuration of a specific Pinecone index.
describe-index-stats
Get statistics about an index, including record counts.
list-indexes
List all of the Pinecone vector indexes in your project.
rerank-documents
Rerank a set of records or documents with a reranking model.
search-docs
Search the official Pinecone documentation for a topic.
search-records
Search index records using text queries and metadata filters.
upsert-records
Insert or update records in an index with integrated inference.