# Pinecone — how to use (mcp.ai)

Connect your Pinecone account and use 48 tools for artificial intelligence straight from your AI agent. Connect with your own API key. Long-term Memory for AI. The Pinecone vector database makes it easy to build high-performance vector search applications. Developer-friendly, fully managed, and easily scalable without infrastructure hassles.

## Option A — via MCP (recommended)
Remote MCP endpoint (HTTP, streamable): `https://api.mcp.ai/p_pinecone?ms=1787291460000`
Add it as a custom/remote MCP connector in your client (Claude, Cursor, VS Code…), then authenticate when prompted. Once connected, ask the agent to use the server's tools (e.g. `pinecone_cancel_bulk_import`).

## Option B — via direct REST API
Base URL: `https://api.mcp.ai/api/pinecone`
Auth: `Authorization: Bearer sk_live_…` — create a workspace API key at https://mcp.ai/settings/api-keys
Discover endpoints: `GET https://api.mcp.ai/api/pinecone/_endpoints`

### Endpoints
- `POST https://api.mcp.ai/api/pinecone/cancel/bulk/import` — Tool to cancel a bulk import operation in Pinecone. Use when you need to stop an ongoing import operation that is not yet finished.
  - body: { id: string, index_host: string }
- `POST https://api.mcp.ai/api/pinecone/chat/assistant` — Tool to chat with a Pinecone assistant and get structured responses with citations. Use when you need to query an assistant that has access to your knowledge base and want to get back answers with doc
  - body: { model?: string, filter?: object, stream?: boolean, messages: object[], temperature?: number, json_response?: boolean, assistant_host: string, assistant_name: string, context_options?: object, include_highlights?: boolean }
- `POST https://api.mcp.ai/api/pinecone/chat/completion/assistant` — Tool to chat with a Pinecone assistant through an OpenAI-compatible interface. Use when you need to interact with a Pinecone assistant that has access to indexed documents and can answer questions bas
  - body: { model?: string, filter?: object, stream?: boolean, messages: object[], temperature?: number, assistant_host: string, assistant_name: string }
- `POST https://api.mcp.ai/api/pinecone/configure/index` — Tool to configure an existing Pinecone index, including pod type, replicas, deletion protection, and tags. Use when you need to scale an index vertically or horizontally, enable/disable deletion prote
  - body: { spec?: object, tags?: object, index_name: string, api_version?: string, deletion_protection?: string }
- `POST https://api.mcp.ai/api/pinecone/create/assistant` — Tool to create a new Pinecone assistant for RAG (Retrieval-Augmented Generation) applications. Use when you need to initialize a new assistant that can have files uploaded and support chat interaction
  - body: { name: string, metadata?: object, api_version?: string, instructions?: string }
- `POST https://api.mcp.ai/api/pinecone/create/backup` — Tool to create a backup of a Pinecone index for disaster recovery and version control. Use when you need to preserve the current state of an index including vectors, metadata, and configuration.
  - body: { name?: string, index_name: string, description?: string }
- `POST https://api.mcp.ai/api/pinecone/create/index` — Tool to create a Pinecone index with specified configuration. Use when you need to initialize a new vector database index for storing and querying embeddings.
  - body: { name: string, spec: object, metric: string, dimension?: integer, vector_type?: string, deletion_protection?: string }
- `POST https://api.mcp.ai/api/pinecone/create/index/for/model` — Tool to create a Pinecone index with integrated embedding model for automatic vectorization. Use when you need to set up a new index that automatically converts text to vectors using a pre-configured 
  - body: { name: string, tags?: object, cloud: string, embed: object, region: string, deletion_protection?: string }
- `POST https://api.mcp.ai/api/pinecone/create/index/from/backup` — Tool to create an index from a backup. Use when you need to restore or duplicate index data from a previously saved backup.
  - body: { name: string, tags?: object, backup_id: string, deletion_protection?: string }
- `POST https://api.mcp.ai/api/pinecone/create/namespace` — Tool to create a namespace within a serverless Pinecone index. Use when you need to organize vectors into isolated partitions.
  - body: { name: string, schema?: object, index_host: string }
- `POST https://api.mcp.ai/api/pinecone/delete/assistant` — Tool to permanently delete a Pinecone assistant. Use when you need to remove an assistant from your project.
  - body: { assistant_name: string }
- `POST https://api.mcp.ai/api/pinecone/delete/backup` — Tool to delete a backup. Use when you need to permanently remove a backup from your project.
  - body: { backup_id: string }
- `POST https://api.mcp.ai/api/pinecone/delete/file` — Tool to delete an uploaded file from a Pinecone assistant. Use when you need to remove a file that was previously uploaded to an assistant.
  - body: { assistant_host: string, assistant_name: string, assistant_file_id: string }
- `POST https://api.mcp.ai/api/pinecone/delete/index` — Tool to permanently delete a Pinecone index. Use when you need to remove an index from your project. Note: Deletion protection and pending collections can prevent deletion.
  - body: { index_name: string }
- `POST https://api.mcp.ai/api/pinecone/delete/namespace` — Tool to permanently delete a namespace from a serverless index. Use when you need to remove an entire namespace and all its data. This operation is irreversible and only supported on serverless indexe
  - body: { namespace: string, index_host: string }
- `POST https://api.mcp.ai/api/pinecone/delete/vectors` — Tool to delete vectors by ID from a Pinecone index. Use when you need to remove specific vectors from a namespace. Supports deletion by IDs, metadata filter, or entire namespace deletion.
  - body: { ids?: string[], filter?: object, namespace?: string, delete_all?: boolean, index_host: string }
- `POST https://api.mcp.ai/api/pinecone/describe/backup` — Tool to retrieve detailed information about a specific backup. Use when you need to check backup status, configuration, or metadata.
  - body: { backup_id: string }
- `POST https://api.mcp.ai/api/pinecone/describe/bulk/import` — Tool to describe a specific bulk import operation in Pinecone. Use when you need to retrieve detailed information about an import's status, progress, timing, and any errors.
  - body: { id: string, index_host: string }
- `POST https://api.mcp.ai/api/pinecone/describe/file` — Tool to get the status and metadata of a file uploaded to a Pinecone assistant. Use when you need to check file processing status, retrieve file metadata, or get a temporary signed URL for accessing t
  - body: { api_version?: string, include_url?: string, assistant_host: string, assistant_name: string, assistant_file_id: string }
- `POST https://api.mcp.ai/api/pinecone/describe/index` — Tool to retrieve full Pinecone index configuration including host and integrated embedding settings. Use when you need to check if an index has integrated inference (embed config) to avoid 'Integrated
  - body: { index_name: string, api_version?: string }
- `POST https://api.mcp.ai/api/pinecone/describe/index/stats` — Tool to get index statistics including vector count per namespace, dimensions, and fullness. Use when you need to understand the contents and status of an index.
  - body: { filter?: object, index_host: string }
- `POST https://api.mcp.ai/api/pinecone/describe/namespace` — Tool to describe a namespace in a serverless index, including the total number of vectors. Use when you need to get metadata about a namespace such as record count and indexed fields.
  - body: { namespace: string, index_host: string }
- `POST https://api.mcp.ai/api/pinecone/describe/restore/job` — Tool to get detailed information about a specific restore job in Pinecone. Use when you need to check the status, progress, or metadata of a restore operation.
  - body: { job_id: string }
- `POST https://api.mcp.ai/api/pinecone/embed` — Tool to generate vector embeddings for input text using Pinecone's hosted embedding models. Use when you need to convert text into vector representations for semantic search or similarity matching.
  - body: { model: string, inputs: object[], parameters?: object }
- `POST https://api.mcp.ai/api/pinecone/fetch/vectors` — Tool to fetch vectors by ID from a Pinecone index. Use when you need to retrieve vector data and metadata for specific vector IDs from a single namespace.
  - body: { ids: string[], namespace?: string, index_host: string }
- `POST https://api.mcp.ai/api/pinecone/get/assistant` — Tool to retrieve the status and details of a Pinecone assistant. Use when you need to check assistant configuration, deployment status, or metadata.
  - body: { api_version?: string, assistant_name: string }
- `POST https://api.mcp.ai/api/pinecone/get/model` — Tool to retrieve detailed information about a specific model hosted by Pinecone. Use when you need to understand model capabilities for embedding and reranking operations.
  - body: { model_name: string, api_version?: string }
- `POST https://api.mcp.ai/api/pinecone/list/assistants` — Tool to list all assistants in a Pinecone project. Use when you need to retrieve all assistants with their configurations and status information.
  - body: { limit?: integer, pagination_token?: string }
- `POST https://api.mcp.ai/api/pinecone/list/bulk/imports` — Tool to list all recent and ongoing bulk import operations in Pinecone. Use when you need to monitor or track the status of data import jobs. Supports pagination with a default limit of 100 imports pe
  - body: { limit?: integer, index_host: string, paginationToken?: string }
- `POST https://api.mcp.ai/api/pinecone/list/collections` — Tool to list all collections in a Pinecone project (pod-based indexes only). Use when you need to view available collections.
- `POST https://api.mcp.ai/api/pinecone/list/files` — Tool to list all files in a Pinecone assistant with optional metadata filtering. Use when you need to retrieve files uploaded to a specific assistant or filter files by metadata properties.
  - body: { limit?: integer, filter?: string, api_version?: string, assistant_host?: string, assistant_name: string, pagination_token?: string }
- `POST https://api.mcp.ai/api/pinecone/list/index/backups` — Tool to list all backups for a specific Pinecone index. Use when you need to view available backups for an index. Supports pagination via limit and paginationToken parameters.
  - body: { limit?: integer, index_name: string, pagination_token?: string }
- `POST https://api.mcp.ai/api/pinecone/list/indexes` — Tool to list all indexes in a Pinecone project. Use when you need to retrieve all indexes with their configurations and status information.
- `POST https://api.mcp.ai/api/pinecone/list/models` — Tool to list all available embedding and reranking models hosted by Pinecone. Use when you need to discover available models or filter by model type (embed/rerank) or vector type (dense/sparse).
  - body: { type?: string, vector_type?: string }
- `POST https://api.mcp.ai/api/pinecone/list/namespaces/operation` — Tool to list all namespaces in a serverless Pinecone index. Use when you need to discover available namespaces for data organization. Returns up to 100 namespaces by default with pagination support.
  - body: { limit?: integer, prefix?: string, index_host: string, api_version?: string, pagination_token?: string }
- `POST https://api.mcp.ai/api/pinecone/list/project/backups` — Tool to list all backups for indexes in a Pinecone project. Use when you need to retrieve backup information across all project indexes. Supports pagination with limit and paginationToken parameters.
  - body: { limit?: integer, paginationToken?: string }
- `POST https://api.mcp.ai/api/pinecone/list/restore/jobs` — Tool to list all restore jobs for a project with pagination support. Use when you need to view the status of restore operations or track restore progress.
  - body: { limit?: integer, paginationToken?: string }
- `POST https://api.mcp.ai/api/pinecone/list/vectors` — Tool to list vector IDs in a Pinecone serverless index. Use when you need to browse or retrieve vector identifiers from a namespace. Supports filtering by prefix and pagination for large result sets.
  - body: { limit?: integer, prefix?: string, namespace?: string, index_host: string, paginationToken?: string }
- `POST https://api.mcp.ai/api/pinecone/query/vectors` — Tool to perform semantic search within a Pinecone index using a query vector. Retrieves IDs and similarity scores of the most similar items, ordered from most to least similar. Either vector or id par
  - body: { id?: string, topK: integer, filter?: object, vector?: number[], indexHost: string, namespace?: string, sparseVector?: object, includeValues?: boolean, includeMetadata?: boolean }
- `POST https://api.mcp.ai/api/pinecone/rerank` — Tool to rerank documents by semantic relevance to a query. Use when you need to order retrieved documents by their semantic relevance to a user's search query using Pinecone's hosted reranking models.
  - body: { model: string, query: string, top_n?: integer, documents: object[], parameters?: object, rank_fields?: string[], return_documents?: boolean }
- `POST https://api.mcp.ai/api/pinecone/retrieve/context/assistant` — Tool to retrieve context snippets from a Pinecone assistant for RAG or agentic workflows. Use when you need to fetch relevant document snippets based on a query or conversation messages to provide con
  - body: { query?: string, top_k?: integer, filter?: object, messages?: object[], multimodal?: boolean, snippet_size?: integer, assistant_host: string, assistant_name: string, include_binary_content?: boolean }
- `POST https://api.mcp.ai/api/pinecone/search/records/namespace` — Tool to search records within a Pinecone namespace using text, vector, or ID query. Use when you need to find similar records based on embeddings or record IDs. Results can optionally be reranked for 
  - body: { query: object, fields?: string[], rerank?: object, namespace: string, index_host: string }
- `POST https://api.mcp.ai/api/pinecone/start/bulk/import` — Tool to start an asynchronous bulk import of vectors from object storage (S3, GCS, or Azure Blob Storage) into a Pinecone index. Use when you need to import large volumes of vectors from external stor
  - body: { uri: string, errorMode?: object, indexHost: string, integrationId?: string }
- `POST https://api.mcp.ai/api/pinecone/update/assistant` — Tool to update an assistant's instructions and configuration in Pinecone. Use when you need to modify an existing assistant's behavior by changing its instructions or metadata. Updates are applied imm
  - body: { metadata?: object, api_version?: string, instructions?: string, assistant_name: string }
- `POST https://api.mcp.ai/api/pinecone/update/vector` — Tool to update a vector in Pinecone by ID. Use to overwrite vector values and/or metadata. Supports bulk updates via metadata filters.
  - body: { id: string, dryRun?: boolean, filter?: object, values?: number[], indexHost: string, namespace?: string, setMetadata?: object, sparseValues?: object }
- `POST https://api.mcp.ai/api/pinecone/upload/file` — Tool to upload a file to a Pinecone assistant for processing. Use when you need to add documents, PDFs, or other files to an assistant for knowledge retrieval or multimodal processing.
  - body: { file?: object, metadata?: string, multimodal?: string, file_content?: object, assistant_host?: string, assistant_name: string }
- `POST https://api.mcp.ai/api/pinecone/upsert/records/namespace` — Tool to upsert text records into a Pinecone namespace. Use when you need to add or update records with automatic text-to-vector conversion.
  - body: { records: object[], namespace: string, index_host: string, api_version?: string }
- `POST https://api.mcp.ai/api/pinecone/upsert/vectors` — Tool to upsert vectors into a Pinecone namespace, overwriting existing vector IDs. Use when you need to add or update vectors with their dense and/or sparse values and metadata.
  - body: { vectors: object[], namespace?: string, index_host: string }

## Example prompts
- "What can I do in Pinecone?"
- "Show me a summary of my Pinecone account"

## More
- Page: https://mcp.ai/pinecone
- Agent spec (llms.txt): https://mcp.ai/pinecone/llms.txt
- Postman collection: https://mcp.ai/pinecone/postman.json