pineconemcp

# 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](https://docs.pinecone.io/guides/operations/mcp-server#tools). > **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:** ```json { "_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