// Curated documentation reference for developers building with Pinecone. Contains links to official docs organized by topic and data format references. Use when writing Pinecone code, looking up API parameters, or needing the correct format for vectors or records.
| name | pinecone:docs |
| description | Curated documentation reference for developers building with Pinecone. Contains links to official docs organized by topic and data format references. Use when writing Pinecone code, looking up API parameters, or needing the correct format for vectors or records. |
| allowed-tools | Read, WebFetch |
A curated index of Pinecone documentation. Fetch the relevant page(s) for the task at hand rather than relying on training data.
Please attempt to fetch the url listed when relevant. If you run into an error, please attempt to append ".md" to the url to retrieve the markdown version of the Docs page.
In case you need it: A full reference to ALL relevant URLs can be found here: https://docs.pinecone.io/llms.txt
Use this as a last resort if you cannot find the relevant page below.
| Topic | URL |
|---|---|
| Quickstart for all languages and coding environments (Cursor, Claude Code, n8n, Python, JavaScript, Java, Go, C#) | https://docs.pinecone.io/guides/get-started/quickstart |
| Pinecone concepts — namespaces, terminology, and key database concepts | https://docs.pinecone.io/guides/get-started/concepts |
| Data modeling for text and vectors | https://docs.pinecone.io/guides/index-data/data-modeling |
| Architecture of Pinecone | https://docs.pinecone.io/guides/get-started/database-architecture |
| Pinecone Assistant overview | https://docs.pinecone.io/guides/assistant/overview |
| Topic | URL |
|---|---|
| Create an index | https://docs.pinecone.io/guides/index-data/create-an-index |
| Index types and conceptual overview | https://docs.pinecone.io/guides/index-data/indexing-overview |
| Integrated inference (built-in embedding models) | https://docs.pinecone.io/guides/index-data/indexing-overview#integrated-embedding |
| Dedicated read nodes — predictable low-latency performance at high query volumes | https://docs.pinecone.io/guides/index-data/dedicated-read-nodes |
| Topic | URL |
|---|---|
| Upsert vectors and text | https://docs.pinecone.io/guides/index-data/upsert-data |
| Multitenancy with namespaces | https://docs.pinecone.io/guides/index-data/implement-multitenancy |
| Topic | URL |
|---|---|
| Semantic search | https://docs.pinecone.io/guides/search/semantic-search |
| Hybrid search | https://docs.pinecone.io/guides/search/hybrid-search |
| Lexical search | https://docs.pinecone.io/guides/search/lexical-search |
Full-text search (preview) — document-schema FTS indexes with text / query_string / dense / sparse scoring | https://docs.pinecone.io/guides/search/full-text-search |
| Metadata filtering — narrow results and speed up searches | https://docs.pinecone.io/guides/search/filter-by-metadata |
| Topic | URL |
|---|---|
| Python SDK reference | https://docs.pinecone.io/reference/sdks/python/overview |
| Example Colab notebooks | https://docs.pinecone.io/examples/notebooks |
| Topic | URL |
|---|---|
| Production checklist — preparing your index for production | https://docs.pinecone.io/guides/production/production-checklist |
| Common errors and what they mean | https://docs.pinecone.io/guides/production/error-handling |
| Targeting indexes correctly — don't use index names in prod | https://docs.pinecone.io/guides/manage-data/target-an-index#target-by-index-host-recommended |
See references/data-formats.md for vector and record schemas.
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.
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.
Create, ingest into, and query a Pinecone full-text-search (FTS) index using the preview API (2026-01.alpha, public preview). Use when the user or agent asks to build a text search index on Pinecone, add dense or sparse vector fields, ingest documents, construct score_by clauses (text / query_string / dense_vector / sparse_vector), or compose with text-match filters ($match_phrase / $match_all / $match_any). Ships `scripts/ingest.py` for safe bulk ingestion (batch_upsert + error inspection + readiness polling); query construction is documented inline in this skill — write `documents.search(...)` calls directly, validated against `pc.preview.indexes.describe(...)` output.
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 tour of Pinecone's tools.
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".
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, and full control over Pinecone resources.