mcp
This skill provides access to Pinecone MCP server tools for managing vector indexes and performing semantic search.
Identity proven by GitHub's verified organisation badge (pinecone-io)
- Agents
- Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode, claude.ai and API
- License
- MIT (permissive)
- Pin
- de67929pinned Oct 9, 2026
- Context cost
- about 990 tokenswhen the skill loads
- GitHub stars
- 23on pinecone-io/gemini-cli-extension
- Scripts
- None found
What it does
“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.”
Generated summary from the skill's own text
This skill provides access to Pinecone MCP server tools for managing vector indexes and performing semantic search. The agent can create integrated indexes with built-in embedding models, upsert records that are automatically embedded, search with reranking, and query multiple indexes simultaneously. It works exclusively with Pinecone's integrated indexes that use built-in embedding models.
When to use it
- Building vector search applications with automatic embedding handling
- Storing and retrieving semantically similar text records
- Searching across multiple Pinecone indexes at once
- Reranking search results or documents for relevance
When not to
- Using standard Pinecone indexes with external embedding models
- Needing to manage indexes outside the MCP server environment
Install
These commands install from the publisher's repository and need no account.
skills CLI
npx skills add pinecone-io/gemini-cli-extension --skill mcpInstalls from the repository's default branch, which may have changed since the commit we checked.
GitHub CLI 2.90 or later
gh skill install pinecone-io/gemini-cli-extension mcp@de6792954ae2a10d5e1a059eaf5ad048af535e17Installs the commit we checked (de67929).
Copy by hand
Download the folder at de67929 and put it in .claude/skills/mcp for Claude Code and in .agents/skills/mcp for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode.
Claude Code plugin
/plugin install pinecone@claude-plugins-officialThe publisher's plugin in Claude's official marketplace. It may hold more than this skill, and we have not pinned it.
Copy for your agent
A prompt for any coding agent. It installs this skill from GitHub at the pinned commit, copies the files as they are, and runs nothing unless you ask.
Prompt
Install the agent skill "mcp" by Pinecone. Source, pinned to commit de6792954ae2a10d5e1a059eaf5ad048af535e17: https://github.com/pinecone-io/gemini-cli-extension/tree/de6792954ae2a10d5e1a059eaf5ad048af535e17/skills/mcp 1. Download that folder at exactly this commit. Do not use the default branch. 2. Copy every file unchanged, keeping the folder structure, into .claude/skills/mcp/ for Claude Code and .agents/skills/mcp/ for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode. Create the folders if they do not exist. 3. Do not edit the files, and do not run any script in the folder unless I ask you to. 4. When you are done, list the files you copied and where you put them. Listing and checks: https://www.agentnative.dev/skills/pinecone/mcp
SKILL.md at the pin
---
name: mcp
description: 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.
---
# 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
From pinecone-io/gemini-cli-extension at de67929, shown as plain text under its license: MIT (permissive). The license text is in LICENSE.
View SKILL.md on GitHub at de67929Files
- SKILL.md4.4 KB · 692ec9bcc739
1 file, 4.4 KB in total. Each file has a sha256 in the pin's manifest; the first 12 characters are shown.
Skill folder on GitHub at de67929Also in
The same SKILL.md content appears in these places too. We list it once and credit the oldest public source.
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Open roles at Pinecone
3 open roles on the jobs board
- Associate Field Engineer
New York City, New York, United States (On-site)
- Senior/Staff Software Engineer, Search & Retrieval Infrastructure
Remote (US) (Remote)
- Senior/Staff Software Engineer, Search & Retrieval Infrastructure
New York City (Remote)