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.

list-indexes

List all indexes in the current Pinecone project.

describe-index

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

Parameters:

• name (required) — Index name

describe-index-stats

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

Parameters:

• name (required) — Index name

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)

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" }

search-records

Semantic text search against an integrated index. Pass plain text — the MCP embeds the query automatically using the index’s model.

Parameters:

• name (required) — Index name
• namespace (required) — Namespace to search
• query.inputs.text (required) — The text query
• query.topK (required) — Number of results to return
• query.filter (optional) — Metadata filter using MongoDB-style operators ($eq, $ne, $in, $gt, $gte, $lt, $lte)
• rerank.model (optional) — Reranking model: bge-reranker-v2-m3, cohere-rerank-3.5, or pinecone-rerank-v0
• rerank.rankFields (optional) — Fields to rerank on (e.g. ["chunk_text"])
• rerank.topN (optional) — Number of results to return after reranking

cascading-search

Search across multiple indexes simultaneously, then deduplicate and rerank results into a single ranked list.

Parameters:

• indexes (required) — Array of { name, namespace } objects to search across
• query.inputs.text (required) — The text query
• query.topK (required) — Number of results to retrieve per index before reranking
• rerank.model (required) — Reranking model: bge-reranker-v2-m3, cohere-rerank-3.5, or pinecone-rerank-v0
• rerank.rankFields (required) — Fields to rerank on
• rerank.topN (optional) — Final number of results to return after reranking

rerank-documents

Rerank a set of documents or records against a query without performing a vector search first.

Parameters:

• model (required) — bge-reranker-v2-m3, cohere-rerank-3.5, or pinecone-rerank-v0
• query (required) — The query to rerank against
• documents (required) — Array of strings or records to rerank
• options.topN (required) — Number of results to return
• options.rankFields (optional) — If documents are records, the field(s) to rerank on