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/pinecone-claude-code-plugin/mcpnpx skills add pinecone-io/pinecone-claude-code-plugin --skill mcpgit clone --depth 1 https://github.com/pinecone-io/pinecone-claude-code-pluginWhat 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 | $0.00080 | $0.01136 |
| Opus 5 | $0.00040 | $0.00568 |
| Sonnet 5 | $0.00016 | $0.00227 |
| Haiku 4.5 | $0.00008 | $0.00114 |
Grade A, and why
pinecone:mcp 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 2d 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- pinecone-mcp — 100% identical, 3 lines differ
- pinecone-mcp — 100% identical, 3 lines differ
- mcp — 98% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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" }
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.
- 2d ago First seen · 108 lines · 80 tokens per session scan A f7c0d254efed
pinecone:mcp is a skill published in the GitHub repository pinecone-io/pinecone-claude-code-plugin (68 stars, last pushed 19d ago), licensed MIT. It adds 80 tokens to every session and 1,136 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
help
Overview of all available Pinecone skills and what a user needs to get started. Invoke when a user asks what skills are available, how to get started with Pinecone, or what they need to set up before using any Pinecone skill.
assistant
Create, manage, and chat with Pinecone Assistants for document Q&A with citations. Handles all assistant operations - create, upload, sync, chat, context retrieval, and list. Recognizes natural language like "create an assistant from my docs", "ask my assistant about X", or "upload my docs to Pinecone".
pinecone-help
Overview of all available Pinecone skills and what a user needs to get started. Invoke when a user asks what skills are available, how to get started with Pinecone, or what they need to set up before using any Pinecone skill.
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…
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-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.