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query

Search Pinecone indexes with natural language via MCP tools.

23|1|Updated Mar 4, 2026
Search Tags:#mcp#query#indexes#pinecone#semantic-search#data-retrieval
One-click install
npx skills add https://github.com/pinecone-io/gemini-cli-extension --skill query-pinecone-io
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: query
Source: https://github.com/pinecone-io/gemini-cli-extension/tree/main/skills/query
Command: npx skills add https://github.com/pinecone-io/gemini-cli-extension --skill query-pinecone-io

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Query integrated indexes using natural language text via the Pinecone MCP server.

Core Features & Use Cases

  • MCP-driven search: Use Pinecone MCP tools to run text-based queries against integrated indexes (e.g., to describe, list, or search by content).
  • Context-aware results: Returns relevant records with scores and metadata for quick decision-making.
  • Use Case: Retrieve product descriptions from an integrated index by asking for "recent product specs".

Quick Start

Ask the assistant to search an integrated index using a natural language query with Pinecone MCP.

Frequently Asked Questions about query

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I query Pinecone indexes using natural language text?▼

To query Pinecone indexes using natural language text, use the Pinecone MCP server to run semantic searches. It processes text queries against integrated indexes and returns relevant records with scores and metadata.

What Pinecone MCP tools are available for semantic search?▼

Available Pinecone MCP tools for semantic search include search-records, list-indexes, describe-index, describe-index-stats, and rerank-documents. These tools enable listing, describing, searching, and reranking index records.

Do I need a Pinecone API key to use MCP search tools?▼

Yes, you need a PINECONE_API_KEY configured in your environment to use Pinecone MCP search tools. The MCP-enabled environment must have the API key set and the Pinecone MCP tools accessible to perform semantic search.

Can I retrieve metadata and scores from Pinecone records using MCP?▼

Yes, you can retrieve metadata and scores from Pinecone records using MCP. Searching integrated indexes with natural language text returns context-aware results that include relevant records, relevance scores, and associated metadata.

How do I list and describe Pinecone indexes through an MCP server?▼

You can list and describe Pinecone indexes through an MCP server by using the list-indexes and describe-index tools. These MCP tools allow you to enumerate available indexes and retrieve detailed configuration information.

What is the best way to rerank documents retrieved from a Pinecone index?▼

The best way to rerank documents retrieved from a Pinecone index is using the rerank-documents MCP tool. It processes the retrieved records to reorder them based on relevance to your natural language query.

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