Model Context Protocol server for Pinecone - enables AI assistants to interact with Pinecone indexes and documentation
Pinecone MCP MCP server exists for a simple reason — assistants are far more useful when they can act on Pinecone MCP directly instead of describing what you should do. Model Context Protocol server for Pinecone - enables AI assistants to interact with Pinecone indexes and documentation.
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 Pinecone MCP is connected, these are the calls the assistant has available:
search-docs — Search the official Pinecone documentationlist-indexes — Lists all Pinecone indexesdescribe-index — Describes the configuration of an indexdescribe-index-stats — Provides statistics about the data in the index, including the number of records and available namespacescreate-index-for-model — Creates a new index that uses an integrated inference model to embed text as vectorsupsert-records — Inserts or updates records in an index with integrated inferencesearch-records — Searches for records in an index based on a text query, using integrated inference for embedding. Has options for metadata filtering and rerankingcascading-search — Searches for records across multiple indexes, deduplicating and reranking the resultsrerank-documents — Reranks a collection of records or text documents using a specialized reranking modelTools — Pinecone Developer MCP Server provides the following tools for AI assistants to use: - search-docs: Search the official Pinecone documentation. -Limitations — Only indexes with integrated inference are supported. Assistants, indexes without integrated inference, standalone embeddings, and vector search areYou will need one environment variable: PINECONE_API_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Installation goes through your MCP client rather than a global install: point it at @pinecone-database/mcp on npm and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Among the knowledge and memory options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Pinecone MCP's toolset — search-docs, list-indexes, describe-index and 8 more — is a fair guide to whether it matches your workflow. It is maintained by pinecone-io; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| 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. |
| Tools | 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 |
| Limitations | Only indexes with integrated inference are supported. Assistants, indexes without integrated inference, standalone embeddings, and vector search are not supported. |
{
"mcpServers": {
"pinecone": {
"command": "npx",
"args": [
"-y", "@pinecone-database/mcp"
],
"env": {
"PINECONE_API_KEY": "<your pinecone api key>"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| PINECONE_API_KEY | Credential the server authenticates with. | Yes |
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.