pinecone-mcp
Reference for the Pinecone MCP server tools. Documents all available tools - list-indexes, describe-index, describe-index-stats, create-index-for-model, upsert-records, search-records, cascading-search, and rerank-documents. Use when an agent needs to understand what Pinecone MCP tools are available, how to use them, or what parameters they accept.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/pinecone-io/skills --skill pinecone-mcp- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads pinecone-mcp automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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 namecloud(required) —aws,gcp, orazureregion(required) — Cloud region (e.g.us-east-1)embed.model(required) — Embedding model:llama-text-embed-v2,multilingual-e5-large, orpinecone-sparse-english-v0embed.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 namenamespace(required) — Namespace to upsert intorecords(required) — Array of records. Each record must have anidor_idfield and contain the text field specified in the index'sfieldMap. Do not nest fields undermetadata— 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 namenamespace(required) — Namespace to searchquery.inputs.text(required) — The text queryquery.topK(required) — Number of results to returnquery.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, orpinecone-rerank-v0rerank.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 acrossquery.inputs.text(required) — The text queryquery.topK(required) — Number of results to retrieve per index before rerankingrerank.model(required) — Reranking model:bge-reranker-v2-m3,cohere-rerank-3.5, orpinecone-rerank-v0rerank.rankFields(required) — Fields to rerank onrerank.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, orpinecone-rerank-v0query(required) — The query to rerank againstdocuments(required) — Array of strings or records to rerankoptions.topN(required) — Number of results to returnoptions.rankFields(optional) — If documents are records, the field(s) to rerank on
FAQ
What does the pinecone-mcp skill do?
Reference for the Pinecone MCP server tools. Documents all available tools - list-indexes, describe-index, describe-index-stats, create-index-for-model, upsert-records, search-records, cascading-search, and rerank-documents. Use when an agent needs to understand what Pinecone MCP tools are available, how to use them, or what parameters they accept.
How do I install pinecone-mcp?
Run npx skills add https://github.com/pinecone-io/skills --skill pinecone-mcp in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/pinecone-mcp/ (personal) or .claude/skills/pinecone-mcp/ (project).
Which AI agents can use pinecone-mcp?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is pinecone-mcp safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains pinecone-mcp?
It is published by pinecone-io in pinecone-io/skills, and it is an official skill from the maker of the technology it covers. It has 262 recorded installs.
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