// 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, or what parameters they accept.
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.
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.
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.
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 | mcp |
| description | 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, or what parameters they accept. |
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-indexesList all indexes in the current Pinecone project.
describe-indexGet configuration details for a specific index — cloud, region, dimension, metric, embedding model, field map, and status.
Parameters:
name (required) — Index namedescribe-index-statsGet statistics for an index including total record count and per-namespace breakdown.
Parameters:
name (required) — Index namecreate-index-for-modelCreate 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, or azureregion (required) — Cloud region (e.g. us-east-1)embed.model (required) — Embedding model: llama-text-embed-v2, multilingual-e5-large, or pinecone-sparse-english-v0embed.fieldMap.text (required) — The record field that contains text to embed (e.g. chunk_text)upsert-recordsInsert 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 an id or _id field and contain the text field specified in the index's fieldMap. Do not nest fields under metadata — put them directly on the record.Example record:
{ "_id": "rec1", "chunk_text": "The Eiffel Tower was built in 1889.", "category": "architecture" }
search-recordsSemantic text search against an integrated index. Pass plain text — the MCP embeds the query automatically using the index's model.
Parameters:
name (required) — Index namenamespace (required) — Namespace to searchquery.inputs.text (required) — The text queryquery.topK (required) — Number of results to returnquery.filter (optional) — Metadata filter using MongoDB-style operators ($eq, $ne, $in, $gt, $gte, $lt, $lte)rerank.model (optional) — Reranking model: bge-reranker-v2-m3, cohere-rerank-3.5, or pinecone-rerank-v0rerank.rankFields (optional) — Fields to rerank on (e.g. ["chunk_text"])rerank.topN (optional) — Number of results to return after rerankingcascading-searchSearch across multiple indexes simultaneously, then deduplicate and rerank results into a single ranked list.
Parameters:
indexes (required) — Array of { name, namespace } objects to search acrossquery.inputs.text (required) — The text queryquery.topK (required) — Number of results to retrieve per index before rerankingrerank.model (required) — Reranking model: bge-reranker-v2-m3, cohere-rerank-3.5, or pinecone-rerank-v0rerank.rankFields (required) — Fields to rerank onrerank.topN (optional) — Final number of results to return after rerankingrerank-documentsRerank a set of documents or records against a query without performing a vector search first.
Parameters:
model (required) — bge-reranker-v2-m3, cohere-rerank-3.5, or pinecone-rerank-v0query (required) — The query to rerank againstdocuments (required) — Array of strings or records to rerankoptions.topN (required) — Number of results to returnoptions.rankFields (optional) — If documents are records, the field(s) to rerank on