pinecone-mcp

Pinecone MCP Tools Reference

The Pinecone MCP server exposes the following tools to AI agents and IDEs. For setup and installation instructions, see the MCP server guide.

Key Limitation: The Pinecone MCP only supports integrated indexes — indexes created with a built-in Pinecone embedding model. It does not work with standard indexes using external embedding models. For those, use the Pinecone CLI.

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list-indexes

List all indexes in the current Pinecone project.

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describe-index

Get configuration details for a specific index — cloud, region, dimension, metric, embedding model, field map, and status.

Parameters:

• name (required) — Index name

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describe-index-stats

Get statistics for an index including total record count and per-namespace breakdown.

Parameters:

• name (required) — Index name

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create-index-for-model

Create a new serverless index with an integrated embedding model. Pinecone handles embedding automatically — no external model needed.

Parameters:

• name (required) — Index name
• cloud (required) — aws, gcp, or azure
• region (required) — Cloud region (e.g. us-east-1)
• embed.model (required) — Embedding model: llama-text-embed-v2, multilingual-e5-large, or pinecone-sparse-english-v0
• embed.fieldMap.text (required) — The record field that contains text to embed (e.g. chunk_text)

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upsert-records

Insert or update records in an integrated index. Records are automatically embedded using the index's configured model.

Parameters:

• name (required) — Index name
• namespace (required) — Namespace to upsert into
• records (required) — Array of records. Each record must have an id or _id field and contain the text field specified in the index's fieldMap. Do not nest fields under metadata — put them directly on the record.

Example record:

{ "_id": "rec1", "chunk_text": "The Eiffel Tower was built in 1889.", "category": "architecture" }