Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/pinecone-io/skills/pinecone-querynpx skills add pinecone-io/skills --skill pinecone-querygit clone --depth 1 https://github.com/pinecone-io/skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pinecone-io/skills/pinecone-query)<a href="https://agentmods.dev/skills/pinecone-io/skills/pinecone-query"><img src="https://agentmods.dev/badge/skills/pinecone-io/skills/pinecone-query.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00072 | $0.01036 |
| Opus 5 | $0.00036 | $0.00518 |
| Sonnet 5 | $0.00014 | $0.00207 |
| Haiku 4.5 | $0.00007 | $0.00104 |
Grade A, and why
pinecone-query scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
91% identical to pinecone:query — 19 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pinecone Query Skill
Search for records in Pinecone integrated indexes using natural language text queries via the Pinecone MCP server.
<<clarify_style>>
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 pinecone-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_KEYenvironment variable is set - Direct them to the MCP setup documentation or the
pinecone-helpskill
- 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 86 lines · 72 tokens per session scan A 4ae80d7947bc
pinecone-query is a skill published in the GitHub repository pinecone-io/skills (15 stars, last pushed yesterday), licensed MIT. It adds 72 tokens to every session and 1,036 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to pinecone:query, differing in 19 lines, and is treated as a copy.
Other skills, from other repositories
pinecone:full-text-search
Create, ingest into, and query a Pinecone full-text-search (FTS) document index using the graduated document-schema API (Python SDK 10.0.0, API version 2026-07). Use when the user or agent asks to build a text search index on Pinecone, add dense or sparse vector fields, ingest documents, construct scoreby clauses…
pinecone:n8n
Build n8n workflows using the Pinecone Assistant node or Pinecone Vector Store node. Use when building RAG pipelines, chat-with-docs workflows, configuring Pinecone nodes in n8n, troubleshooting Pinecone n8n nodes, or asking about best practices for Pinecone in n8n.
pinecone:quickstart
Interactive Pinecone quickstart for new developers. Choose between two paths - Database (create an integrated index, upsert data, and query using Pinecone MCP + Python) or Assistant (create a Pinecone Assistant for document Q&A). Use when a user wants to get started with Pinecone for the first time or wants a guided…
pinecone:cli
Guide for using the Pinecone CLI (pc) to manage Pinecone resources from the terminal. The CLI supports ALL index types (standard, integrated, sparse) and all vector operations — unlike the MCP which only supports integrated indexes. Use for batch operations, vector management, backups, namespaces, CI/CD automation…
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…
pinecone:query
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 pinecone:cli skill instead. Requires PINECONEAPIKEY environment…