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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, or what parameters they accept.\nallowed-tools: Read\n---\n\n# Pinecone MCP Tools Reference\n\nThe Pinecone MCP server exposes the following tools to AI agents and IDEs. For setup and installation instructions, see the [MCP server guide](https:\u002F\u002Fdocs.pinecone.io\u002Fguides\u002Foperations\u002Fmcp-server#tools).\n\n> **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. 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Pinecone handles embedding automatically — no external model needed.\n\n**Parameters:**\n- `name` (required) — Index name\n- `cloud` (required) — `aws`, `gcp`, or `azure`\n- `region` (required) — Cloud region (e.g. `us-east-1`)\n- `embed.model` (required) — Embedding model: `llama-text-embed-v2`, `multilingual-e5-large`, or `pinecone-sparse-english-v0`\n- `embed.fieldMap.text` (required) — The record field that contains text to embed (e.g. `chunk_text`)\n\n---\n\n## `upsert-records`\n\nInsert or update records in an integrated index. Records are automatically embedded using the index's configured model.\n\n**Parameters:**\n- `name` (required) — Index name\n- `namespace` (required) — Namespace to upsert into\n- `records` (required) — Array of records. Each record must have an `id` or `_id` field and contain the text field specified in the index's `fieldMap`. 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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 \u002F query_string \u002F dense_vector \u002F sparse_vector), or compose with text-match filters ($match_phrase \u002F $match_all \u002F $match_any). Ships `scripts\u002Fingest.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.",{"slug":9,"name":10,"logoUrl":11,"githubOrg":12},[1018,1019,1020],{"name":15,"slug":16,"type":17},{"name":10,"slug":9,"type":17},{"name":22,"slug":23,"type":17},"2026-07-13T06:02:18.270055",{"slug":1023,"name":1024,"fn":1025,"description":1026,"org":1027,"tags":1028,"stars":25,"repoUrl":26,"updatedAt":1032},"pineconehelp","pinecone:help","provide Pinecone skill guidance","Overview of all available Pinecone skills and what a user needs to get started. 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