query: Design & UI for AI Agents | OpenAgentSkill

Overview

Query integrated indexes using text with Pinecone MCP. IMPORTANT - This skill ONLY works with integrated indexes (indexes with built-in Pinecone embedding models like multilingual-e5-large). For standard indexes or advanced vector operations, use the CLI skill instead. Requires PINECONE_API_KEY environment variable and Pinecone MCP server to be configured.

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Pinecone Query Skill

Search for records in Pinecone integrated indexes using natural language text queries via the Pinecone MCP server.

What is this skill for?

This skill provides a simple way to query integrated indexes (indexes with built-in Pinecone embedding models) using text queries. The MCP server automatically converts your text into embeddings and searches the index.

Prerequisites

Required:

✅ Pinecone MCP server must be configured - Check if MCP tools are available
✅ PINECONE_API_KEY environment variable must be set - Get a free API key at https://app.pinecone.io/?sessionType=signup
✅ Index must be an integrated index - Uses Pinecone embedding models (e.g., multilingual-e5-large, llama-text-embed-v2, pinecone-sparse-english-v0)

When NOT to use this skill

Use the CLI skill instead if:

❌ Your index is a standard index (no integrated embedding model)
❌ You need to query with custom vector values (not text)
❌ You need advanced vector operations (fetch by ID, list vectors, bulk operations)
❌ Your index uses third-party embedding models (OpenAI, HuggingFace, Cohere)

MCP Limitation: The Pinecone MCP currently only supports integrated indexes. For all other use cases, use the Pinecone CLI skill.

How it works

Utilize Pinecone MCP's search-records tool to search for records within a specified Pinecone integrated index using a text query.

Workflow

IMPORTANT: Before proceeding, verify the Pinecone MCP tools are available. If MCP tools are not accessible:

Inform the user that the Pinecone MCP server needs to be configured
Check if PINECONE_API_KEY environment variable is set
Direct them to the MCP setup documentation or the help skill

Parse the user's input for:
- query (required): The text to search for.
- index (required): The name of the Pinecone index to search.
- namespace (optional): The namespace within the index.
- reranker (optional): The reranking model to use for improved relevance.
If the user omits required arguments:
- If only the index name is provided, use the describe-index tool to retrieve available namespaces and ask the user to choose.
- If only a query is provided, use list-indexes to get available indexes, ask the user to pick one, then use describe-index for namespaces if needed.
Call the search-records tool with the gathered arguments to perform the search.
Format and display the returned results in a clear, readable table including field highlights (such as ID, score, and relevant metadata).

Troubleshooting

PINECONE_API_KEY is required. Get a free key at https://app.pinecone.io/?sessionType=signup

If you get an access error, the key is likely missing. Ask the user to set it and restart their IDE or agent session:

Terminal: export PINECONE_API_KEY="your-key"
IDE without shell inheritance: add PINECONE_API_KEY=your-key to a .env file

IMPORTANT At the moment, the query action can only be used with integrated indexes, which use hosted Pinecone embedding models to embed and search for data. If a user attempts to query an index that uses a third party API model such as OpenAI, or HuggingFace embedding models, remind them that this capability is not available yet with the Pinecone MCP server.

If required arguments are missing, prompt the user to supply them, using Pinecone MCP tools as needed (e.g., list-indexes, describe-index).
Guide the user interactively through argument selection until the search can be completed.
If an invalid value is provided for any argument (e.g., nonexistent index or namespace), surface the error and suggest valid options.

Tools Reference

search-records: Search records in a given index with optional metadata filtering and reranking.
list-indexes: List all available Pinecone indexes.
describe-index: Get index configuration and namespaces.
describe-index-stats: Get stats including record counts and namespaces.
rerank-documents: Rerank returned documents using a specified reranking model.
Ask the user interactively to clarify missing information when needed.

View source ↗

File metadata

name: query
description: Query integrated indexes using text with Pinecone MCP. IMPORTANT - This skill ONLY works with integrated indexes (indexes with built-in Pinecone embedding models like multilingual-e5-large). For standard indexes or advanced vector operations, use the CLI skill instead. Requires PINECONE_API_KEY environment variable and Pinecone MCP server to be configured.

View original text

---
name: query
description: Query integrated indexes using text with Pinecone MCP. IMPORTANT - This skill ONLY works with integrated indexes (indexes with built-in Pinecone embedding models like multilingual-e5-large). For standard indexes or advanced vector operations, use the CLI skill instead. Requires PINECONE_API_KEY environment variable and Pinecone MCP server to be configured.
---

# Pinecone Query Skill

Search for records in Pinecone integrated indexes using natural language text queries via the Pinecone MCP server.

## What is this skill for?

This skill provides a simple way to query **integrated indexes** (indexes with built-in Pinecone embedding models) using text queries. The MCP server automatically converts your text into embeddings and searches the index.

### Prerequisites

**Required:**
1. ✅ **Pinecone MCP server must be configured** - Check if MCP tools are available
2. ✅ **PINECONE_API_KEY environment variable must be set** - Get a free API key at https://app.pinecone.io/?sessionType=signup
3. ✅ **Index must be an integrated index** - Uses Pinecone embedding models (e.g., multilingual-e5-large, llama-text-embed-v2, pinecone-sparse-english-v0)

### When NOT to use this skill

**Use the CLI skill instead if:**
- ❌ Your index is a standard index (no integrated embedding model)
- ❌ You need to query with custom vector values (not text)
- ❌ You need advanced vector operations (fetch by ID, list vectors, bulk operations)
- ❌ Your index uses third-party embedding models (OpenAI, HuggingFace, Cohere)

**MCP Limitation**: The Pinecone MCP currently only supports integrated indexes. For all other use cases, use the Pinecone CLI skill.

## How it works

Utilize Pinecone MCP's `search-records` tool to search for records within a specified Pinecone integrated index using a text query.

## Workflow

**IMPORTANT: Before proceeding, verify the Pinecone MCP tools are available.** If MCP tools are not accessible:
- Inform the user that the Pinecone MCP server needs to be configured
- Check if `PINECONE_API_KEY` environment variable is set
- Direct them to the MCP setup documentation or the `help` skill

1. Parse the user's input for:
- `query` (required): The text to search for.
- `index` (required): The name of the Pinecone index to search.
- `namespace` (optional): The namespace within the index.
- `reranker` (optional): The reranking model to use for improved relevance.

2. If the user omits required arguments:
- If only the index name is provided, use the `describe-index` tool to retrieve available namespaces and ask the user to choose.
- If only a query is provided, use `list-indexes` to get available indexes, ask the user to pick one, then use `describe-index` for namespaces if needed.

3. Call the `search-records` tool with the gathered arguments to perform the search.

4. Format and display the returned results in a clear, readable table including field highlights (such as ID, score, and relevant metadata).

---

## Troubleshooting

**`PINECONE_API_KEY` is required.** Get a free key at https://app.pinecone.io/?sessionType=signup

If you get an access error, the key is likely missing. Ask the user to set it and restart their IDE or agent session:
- Terminal: `export PINECONE_API_KEY="your-key"`
- IDE without shell inheritance: add `PINECONE_API_KEY=your-key` to a `.env` file

**IMPORTANT** At the moment, the query action can only be used with integrated indexes, which use hosted Pinecone embedding models to embed and search for data.
If a user attempts to query an index that uses a third party API model such as OpenAI, or HuggingFace embedding models, remind them that this capability is not available yet
with the Pinecone MCP server.

- If required arguments are missing, prompt the user to supply them, using Pinecone MCP tools as needed (e.g., `list-indexes`, `describe-index`).
- Guide the user interactively through argument selection until the search can be completed.
- If an invalid value is provided for any argument (e.g., nonexistent index or namespace), surface the error and suggest valid options.

## Tools Reference

- `search-records`: Search records in a given index with optional metadata filtering and reranking.
- `list-indexes`: List all available Pinecone indexes.
- `describe-index`: Get index configuration and namespaces.
- `describe-index-stats`: Get stats including record counts and namespaces.
- `rerank-documents`: Rerank returned documents using a specified reranking model.
- Ask the user interactively to clarify missing information when needed.

---

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Low GitHub adoption signal
AI review approval is missing
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 23 GitHub stars
Stars/forks activity: 23 stars, 2 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.

Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: pinecone-io/gemini-cli-extension
License: MIT
Version: Unknown
Last GitHub push: Aug 14, 2026
Registry updated: Sep 13, 2026
Instruction path: skills/query/SKILL.md @ de6792954ae2

Version reported in registry metadata; check source releases before relying on it.

Quality

52/100

Needs review

Trust

55/100

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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Source: pinecone-io/gemini-cli-extension
Indexed by: OpenAgentSkill community index

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