update_vector
Update Vector
Update an existing vector's values, sparse values, or metadata in a Pinecone index. Use this to modify a single vector without needing to re-upsert the entire record.
Supported Tools
describe_index_stats
Index Stats
Get statistics about a Pinecone index including total vector count, vector count per namespace, dimension, fullness, and metric. Use this to monitor index usage and capacity.
query_vectors
Query Vectors
Search for the most similar vectors in a Pinecone index. Query by providing a dense vector or an existing vector ID. Results include similarity scores and optionally the vector values and metadata. Supports metadata filtering and sparse vectors for hybrid search.
generate_embeddings
Generate Embeddings
Generate vector embeddings from text using Pinecone's hosted embedding models. Returns dense or sparse vectors that can be stored in an index or used for queries. Available models include `llama-text-embed-v2` (dense, high-performance), `multilingual-e5-large` (dense, multilingual), and `pinecone-sparse-english-v0` (sparse, keyword search).
list_vector_ids
List Vector IDs
List vector IDs in a Pinecone serverless index with optional namespace and prefix filtering. Returns paginated results. Use this to discover vector IDs before fetching or deleting specific vectors.
upsert_vectors
Upsert Vectors
Insert or update vector records in a Pinecone index. Each vector has a unique ID and dense/sparse embedding values with optional metadata. If a vector with the same ID already exists, it will be overwritten. Supports batch upsert of up to 1000 vectors.
delete_vectors
Delete Vectors
Remove vectors from a Pinecone index. Delete specific vectors by ID, delete by metadata filter, or delete all vectors in a namespace. Useful for cleaning up data, removing outdated records, or clearing entire namespaces.
create_index
Create Index
Create a new Pinecone vector index. Prefer serverless indexes for new projects; BYOC is supported when an environment has already been provisioned. Pod-based indexes are legacy and unavailable to new Pinecone customers.
configure_index
Configure Index
Describe, update, or delete an existing Pinecone index. Update deletion protection, tags, legacy pod replicas, or integrated embedding field/read/write parameters.
fetch_vectors
Fetch Vectors
Retrieve specific vectors by their IDs from a Pinecone index. Returns the full vector data including values, sparse values, and metadata. Use this when you know the exact vector IDs you want to look up.
list_indexes
List Indexes
List all vector indexes in the current Pinecone project. Returns index names, dimensions, metrics, hosting details, and operational status. Use this to discover available indexes before performing vector operations.
rerank
Rerank
Rerank a list of documents by relevance to a query using Pinecone's hosted reranking models (e.g. `bge-reranker-v2-m3`). Returns documents sorted by relevance score. Use after an initial retrieval step to improve search quality.
chat_with_assistant
Chat with Assistant
Ask questions to a Pinecone Assistant and receive context-aware answers grounded in uploaded documents, with inline citations. Supports multi-turn conversations, metadata-based document filtering, model selection, and highlight/context controls.
manage_assistant
Manage Assistant
Create, list, describe, update, or delete Pinecone Assistants. Assistants provide RAG-based document Q&A powered by uploaded documents. Can be deployed in US or EU regions.
search_records
Search Records
Search a Pinecone namespace with text, a vector, or a record ID. This endpoint supports integrated-embedding text search and optional reranking.
get_assistant_context
Get Assistant Context
Retrieve context snippets from a Pinecone Assistant without asking the assistant to generate an answer. Use this for RAG workflows that pass retrieved snippets to another model or agent.
manage_assistant_files
Manage Assistant Files
List, upload, upsert, describe, or delete files in a Pinecone Assistant, and inspect asynchronous assistant file operations.
create_integrated_index
Create Integrated Index
Create a Pinecone serverless index with integrated embedding. Use this when users want to upsert source text and search with text while Pinecone automatically generates vectors from a hosted embedding model.
manage_namespaces
Manage Namespaces
Create, list, describe, or delete namespaces in a Pinecone serverless index. Namespaces partition records for multitenancy and targeted search.
fetch_vectors_by_metadata
Fetch Records by Metadata
Fetch Pinecone records from a namespace by metadata filter. Use this when you need complete records matching metadata conditions and do not know their IDs yet.
upsert_text_records
Upsert Text Records
Upsert text records into a Pinecone integrated-embedding index. Pinecone converts the configured text field to vectors automatically and stores all other fields as metadata.
More integrations teams use with Pinecone
GitHub
Manage repositories, issues, and pull requests. Create and configure branches, star repositories, review code, and merge changes. Automate CI/CD workflows with GitHub Actions, manage workflow runs, secrets, and artifacts. Track issues with labels, milestones, and assignees. Search across code, repositories, issues, and users. Manage organizations, teams, and memberships. Create and manage projects, gists, packages, deployments, and environments. Access security alerts including code scanning, secret scanning, and Dependabot alerts. Read and write file contents in repositories. Manage webhooks, notifications, and codespaces.
Sharepoint
Manage SharePoint sites, document libraries, lists, and files. Create, read, update, and delete lists and list items with custom columns. Upload, download, move, copy, and version files in document libraries. Search across sites, files, folders, lists, and list items using Microsoft Search. Manage permissions at site, list, and item levels with granular access control. Define and manage content types and site columns. Subscribe to webhooks for list and library change notifications. Retrieve site properties and search for sites across Microsoft 365.
Salesforce
Manage CRM data including Accounts, Contacts, Leads, Opportunities, Cases, and custom objects. Create, read, update, and delete records. Query data using SOQL and search across objects using SOSL. Perform bulk data operations for large-scale imports, exports, and migrations. Execute composite requests to batch multiple operations in a single API call. Access analytics, reports, and dashboards. Manage files and attachments associated with records. Interact with Chatter feeds, posts, and groups for social collaboration. Subscribe to real-time change events via Change Data Capture and Platform Events. Manage org metadata including custom objects, fields, layouts, and workflows. Query data using GraphQL for precise data retrieval across related objects.
Airtable
Create, read, update, and delete records in Airtable bases and tables. Manage base schemas including creating tables and fields. Filter records using formulas, sort by fields, and scope queries to specific views. Upsert records to find, create, or update in a single call. Upload attachments to records, read and write record comments, list accessible bases, and receive real-time base change events through webhooks.
Bitbucket
Manage Git repositories, pull requests, and CI/CD pipelines on Bitbucket Cloud. Create, fork, and configure repositories within workspaces and projects. Create, review, approve, merge, and decline pull requests with inline code comments. Browse source code, list commits, and manage branches and tags. Track issues with the built-in issue tracker. Trigger, monitor, and manage Bitbucket Pipelines. List workspace members, configure repository default reviewers and branch restrictions, create and manage repository webhooks, and search code across repositories.
Heroku
Deploy, manage, and scale applications on Heroku's cloud platform. Create and configure apps, scale dynos, provision add-ons (databases, caching, etc.), manage configuration variables, build and release code, add custom domains and SSL certificates, manage collaborators and team permissions, configure pipelines for continuous delivery, set up log drains, and sync data with Salesforce via Heroku Connect. Subscribe to webhooks for real-time notifications on app changes, builds, releases, dyno lifecycle events, and more.
Technical notes for Pinecone
Manage Pinecone vector database indexes and records for AI applications. Create and configure serverless, BYOC, integrated-embedding, and legacy pod indexes. Upsert, search, fetch, update, list, and delete vectors with metadata filtering and namespace partitioning. Work with integrated-embedding text records, hosted embedding/reranking models, and Pinecone Assistants with files, context retrieval, and grounded chat.
Frequently asked questions
Common questions about connecting Pinecone to AI agents with Metorial.
Can Metorial connect Pinecone to AI agents?
Yes. Metorial connects AI agents to Pinecone through a governed integration layer, so teams can use the provider while keeping access controlled and observable.Does the Pinecone integration work with MCP?
Metorial is MCP compatible and lets teams expose approved provider tools to MCP-capable agents and clients through a controlled access layer.How does Metorial control access to Pinecone?
Metorial applies policies across users, groups, providers, agents, and individual tools, then records the context around every agent interaction.Can teams trace Pinecone activity from agents?
Yes. Metorial records provider activity so teams can inspect tool calls, troubleshoot integrations, and give security teams the visibility they need.