# Pinecone — MCP Server

> Build and query Pinecone indexes without leaving the conversation.

**Source:** https://mcpplaygroundonline.com/mcp-servers/pinecone  
**Transport:** stdio  
**Requires auth:** Yes

---

## What it does

Two things make this server useful rather than merely convenient. First, search-docs puts current Pinecone documentation in front of the model, which matters because the SDK surface has changed substantially across versions and a model working from memory will write code against an API that no longer exists. Second, create-index-for-model creates an index with integrated inference configured, meaning Pinecone embeds text server-side — so upsert-records and search-records take plain text and the agent never constructs a vector. On top of that, cascading-search queries several indexes and merges the results, and rerank-documents applies a reranking model to a result set, which is the cheapest meaningful quality win in most retrieval pipelines. Pinecone also publishes a separate Assistant MCP server for querying a Pinecone Assistant knowledge base; the Developer server is the one you want when you are building.

## Tools exposed

- search-docs — search current Pinecone documentation before writing code
- list-indexes / describe-index / describe-index-stats — inspect what exists
- create-index-for-model — create an index with integrated embedding configured
- upsert-records — write plain text records, embedded server-side
- search-records — semantic search over an index namespace
- cascading-search — query multiple indexes and merge the results
- rerank-documents — apply a reranking model to an existing result set

## Example queries you can run

- "Create an index for this dataset and upsert these documents into it."
- "Search the docs for how namespaces interact with metadata filtering, then show me the code."
- "Query the index for passages about refund policy and rerank the top 20."
- "How many records are in each namespace of my production index?"

## Details

- **Recommended model:** anthropic/claude-sonnet-4.5 — This server is used while writing retrieval code. Sonnet 4.5 checks the docs tool before generating SDK calls, which is exactly the behaviour that makes it worth installing.
- **Transport:** stdio
- **Authentication:** Required — A Pinecone API key from the console. Documentation search works without one; everything touching an index needs it.
- **Official source:** [Pinecone MCP server — official documentation](https://docs.pinecone.io/guides/operations/mcp-server)

## Connecting to Pinecone

### Environment variables

- `PINECONE_API_KEY` (required) — API key from the Pinecone console. Scope it to the project holding the indexes the agent should reach.

### Client configuration

**npx (recommended)**

The Developer MCP server — docs, indexes, records and reranking.

```
{
  "mcpServers": {
    "pinecone": {
      "command": "npx",
      "args": ["-y", "@pinecone-database/mcp"],
      "env": { "PINECONE_API_KEY": "YOUR_API_KEY" }
    }
  }
}
```

## Frequently asked questions

### What is the Pinecone MCP server?

It is Pinecone’s official Developer MCP server, published as @pinecone-database/mcp. It gives an AI coding agent tools to search Pinecone documentation, create and inspect indexes, upsert records, run semantic search and rerank results.

### Do I need to create embeddings before upserting?

Not if the index was created with create-index-for-model. That configures integrated inference, so Pinecone embeds text server-side and upsert-records and search-records both work in plain text. For an index you created yourself without integrated embedding, you still supply vectors.

### What is the difference between the Developer and Assistant MCP servers?

The Developer server is for building — docs, index management, records, search. The Assistant server queries a Pinecone Assistant knowledge base you have already built. Install the Developer server while writing the application, the Assistant one when consuming it.

### Why does the docs tool matter so much?

Because the Pinecone SDK has changed shape across major versions, and a model relying on training data will confidently produce calls from an older API. Searching the live documentation first is what stops generated retrieval code from failing on the first run.

### How does Pinecone compare to Qdrant for agent memory?

Pinecone is fully managed with integrated embedding and reranking, so there is less to operate. Qdrant can run embedded on local disk with no service at all and is open source. For a hosted production RAG stack Pinecone is the lower-effort choice; for local-first or self-hosted work, Qdrant.

---

_Test this server across 40+ models on MCP Playground: https://mcpplaygroundonline.com/mcp-servers/pinecone — free, no install._