Pinecone

Pinecone MCP integration

Runs similarity searches and reranks results, upserts records with generated embeddings, provisions indexes and namespaces, and runs backups and imports.

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30actions available

Three actions you can hand over today

Every action runs live through MCP. Nothing to build, nothing to maintain.

  • Search records in namespace

    Lyro performs semantic search on a Pinecone namespace using text, vector, or ID queries to find relevant knowledge. The customer gets fast, ranked results with exact relevance to their question.

  • Upsert records to namespace

    Lyro adds or updates text records in a Pinecone namespace in bulk. The knowledge base stays fresh and the agent can immediately reference the updated information.

  • Generate embeddings

    Lyro converts text input into vector embeddings using Pinecone's hosted models. The vectors are ready for search or storage in the vector database.

See all 30 actions

How businesses use Pinecone + Lyro

Each card is one request a support team gets, and the Pinecone actions Lyro runs to close it.

  • Find the records that actually match the question

    Lyro searches a namespace by text, vector, or record ID, queries an index directly with a query vector for the closest matches and their scores, then reranks the results by semantic relevance.

    Search Records in NamespaceQuery VectorsRerank Documents

  • Get new content into the index

    Lyro upserts text records into a namespace with automatic text-to-vector conversion, generates embeddings through a hosted model when the vectors are needed directly, and overwrites a vector's values or metadata by ID.

    Upsert Records to NamespaceGenerate EmbeddingsUpdate Vector

  • Provision the index a workload needs

    Lyro creates an index with the configuration you specify or with an integrated embedding model for automatic vectorisation, then carves out the namespaces that keep separate datasets isolated.

    Create IndexCreate Index with Embedding ModelCreate Namespace

  • Move and protect data at index scale

    Lyro takes a backup of an index for recovery or versioning, starts an asynchronous bulk import from object storage, and reports on the import's progress or cancels it if it is loading the wrong data.

    Create BackupStart Bulk ImportDescribe Bulk Import

How it works

Get started in 3 steps

Connect once, then just ask. There is no workflow builder to learn and nothing to maintain — Lyro reads the Pinecone actions it has and picks the ones a request needs.

  1. 01

    Connect Pinecone

    Authorize the Pinecone account your team already uses — one consent screen, no API keys, no mapping tables. Lyro can only do what you granted that account, and you can disconnect it at any time.

  2. 02

    Tell your agent what you need

    Describe the job the way you would hand it to a teammate. Lyro maps it to the Pinecone actions that close it and chains as many as the request needs.

  3. 03

    Watch it work

    The agent runs the actions inside the conversation the customer is already in, so nobody copies data between tabs and your team can take over at any point.

    Get started free
Pinecone · Lyro

Everything else about Pinecone

Setup, permissions, and the limits of what Lyro can do inside Pinecone.

  • Start Bulk Import pulls vectors asynchronously from object storage - S3, Google Cloud Storage, or Azure Blob Storage - into an index. Describe Bulk Import returns its status, progress, and timing while it runs, and Cancel Bulk Import stops an operation that has not finished yet.

Every action available in Pinecone

All 30 actions your agent can call on Pinecone, straight from the live MCP connection.

  • Cancel bulk import

    Cancel a bulk import operation in Pinecone.

  • Configure index

    Configure an existing Pinecone index, including pod type, replicas, deletion protection, and tags.

  • Create backup

    Create a backup of a Pinecone index for disaster recovery and version control.

  • Create index

    Create a Pinecone index with specified configuration.

  • Create index with embedding model

    Create a Pinecone index with integrated embedding model for automatic vectorization.

  • Create index from backup

    Create an index from a backup.

  • Create namespace

    Create a namespace within a serverless Pinecone index.

  • Delete index

    Permanently delete a Pinecone index.

  • Delete namespace

    Permanently delete a namespace from a serverless index.

  • Describe backup

    Retrieve detailed information about a specific backup.

  • Describe bulk import

    Describe a specific bulk import operation in Pinecone.

  • Describe index stats

    Get index statistics including vector count per namespace, dimensions, and fullness.

  • Describe restore job

    Get detailed information about a specific restore job in Pinecone.

  • Generate embeddings

    Generate vector embeddings for input text using Pinecone's hosted embedding models.

  • Get model information

    Retrieve detailed information about a specific model hosted by Pinecone.

  • List bulk imports

    List all recent and ongoing bulk import operations in Pinecone.

  • List collections

    List all collections in a Pinecone project (pod-based indexes only).

  • List index backups

    List all backups for a specific Pinecone index.

  • List indexes

    List all indexes in a Pinecone project.

  • List available models

    List all available embedding and reranking models hosted by Pinecone.

  • List namespaces

    List all namespaces in a serverless Pinecone index.

  • List project backups

    List all backups for indexes in a Pinecone project.

  • List restore jobs

    List all restore jobs for a project with pagination support.

  • List vectors

    List vector IDs in a Pinecone serverless index.

  • Query vectors

    Perform semantic search within a Pinecone index using a query vector.

  • Rerank documents

    Rerank documents by semantic relevance to a query.

  • Search records in namespace

    Search records within a Pinecone namespace using text, vector, or ID query.

  • Start bulk import

    Start an asynchronous bulk import of vectors from object storage (S3, GCS, or Azure Blob Storage) into a Pinecone index.

  • Update vector

    Update a vector in Pinecone by ID.

  • Upsert records to namespace

    Upsert text records into a Pinecone namespace.

The tools Pinecone sits next to

Same connection, same setup. Pick the next one your team already uses.

Browse every integration

Ready to connect Pinecone?

Authorize the account and your agent has all 30 actions from the first conversation.

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Support agent working at a laptop next to the Lyro mascot