Skip to content
Vinkius

Pinecone Connector for AI agents.

7 live capabilities

Manage your vector database and query embeddings using natural language.

Live agent request Pinecone / Connector

Waiting for input…

AI Agent

Why people use Pinecone

Pinecone : Stop wasting time on manual vector database audits

With this Connector, you just ask your agent to check the stats or find a specific vector. You get the answer in the same window where you're already building your app. It turns a five-minute chore into a five-second conversation.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get a conversational interface for your vector database.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Testing RAG retrieval

    An engineer asks the agent to find similar vectors for a query to see if the retrieved context is relevant for the prompt.

  2. Real-world use case 02

    Configuration audit

    An architect asks the agent to describe the index configuration to verify the mathematical dimensions and pod settings.

  3. Real-world use case 03

    Targeted retrieval

    A developer uses fetch_vectors to pull specific data points by their unique IDs for a precise data check.

Complete set · 7capabilities

The complete Pinecone capability set.

These are the exact actions your AI can choose when you ask it to work with Pinecone.

Capability set01 / 02

01—04

4 capabilities in this set.

Part of 7 available through Pinecone.

  1. 01 Capability

    Delete vectors

    Removes specific vectors from an index to free up space or clear data. This is useful for maintaining storage hygiene.

  2. 02 Capability

    Describe index

    Shows the configuration details and topology of a specific index. Use this to verify your pod settings and dimensions.

  3. 03 Capability

    Fetch vectors

    Retrieves specific vectors using their unique IDs for precise lookups. It's perfect for pinpointing exact records.

  4. 04 Capability

    Get index stats

    Pulls real-time health checks and capacity limits for your pods. This helps you monitor your storage usage at a glance.

Capability set02 / 02

05—07

3 capabilities in this set.

Part of 7 available through Pinecone.

  1. 05 Capability

    List collections

    Lists all index collections to help you organize snapshot arrays. Use it to see your grouped data structures.

  2. 06 Capability

    Query vectors

    Finds and returns the most similar vectors and metadata for a given input. This is the core capability for semantic search.

  3. 07 Capability

    List indexes

    Shows every index currently existing in your Pinecone environment. It gives you a clear view of your entire vector store.

Set up in minutes

One URL. Then ask Pinecone to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Pinecone from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_cMLbuhs0rnfwH86edlO4MVqnOLr94ybdUvJavFsC/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it Pinecone, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Pinecone for the conversation.

Where the request belongs

Work Pinecone can move forward.

Built around the request

For the AI engineer who's tired of writing boilerplate Python scripts to test RAG relevance or the data custodian who needs to audit vector counts across multiple environments quickly.

01

AI/ML Engineer

Testing how well semantic chunks are being retrieved during RAG development without writing test scripts.

02

Data Custodian

Auditing storage limits and cleaning up old vectors across production indexes via terminal prompts.

03

Agent Builder

Building dynamic knowledge retrieval systems that query vector stores on demand.

When one Connector is not enough

Combine Pinecone with the systems that finish the task.

View all recipes
01

Build Document Intelligence Using Connectors

You have 500 PDFs, contracts and reports that contain critical business knowledge locked inside files nobody reads , Unstructured extracts the content, Pinecone makes it searchable, and Notion indexes every document

 02

Track Competitor Moves Using Connectors

Google Alerts sends you irrelevant noise. Exa understands meaning , it finds the blog post where your competitor quietly announced a pricing change buried in paragraph 7 of a product update, because semantic search reads intent, not just keywords

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

Browse Connectors
Chroma (Vector DB) logo
01 7 capabilities

Chroma (Vector DB)

Manage vector embeddings via Chroma. list collections, query embeddings, and audit document counts directly from any AI agent.

View Connector
Qdrant logo
02 7 capabilities

Qdrant

Empower your AI to interact directly with your Qdrant vector database. query clusters, perform similarity searches, and manage collections effortlessly.

View Connector
Zilliz Cloud logo
03 10 capabilities

Zilliz Cloud

Manage vector collections and perform similarity searches via Zilliz Cloud.

View Connector
OpenSearch Vector logo
04 6 capabilities

OpenSearch Vector

Run k-NN vector searches on OpenSearch. create indexes, upsert embeddings, query similar documents, and manage your vector store from any AI agent.

View Connector
Milvus (Open-Source Vector Database) logo
05 7 capabilities

Milvus (Open-Source Vector Database)

Manage vector storage via Milvus. perform ANN searches, query scalar entities, and audit collections.

View Connector
Supabase Vector logo
06 7 capabilities

Supabase Vector

Connect your AI to Supabase Vector. Execute pgvector semantic searches, manage embeddings, and run relational database queries directly from your terminal.

View Connector

Bring your own AI

Change the model, client or framework. Keep Pinecone connected.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • VS Code
  • Windsurf
  • ZCode
  • Cline
  • Zed
  • Continue
  • Kiro
  • Roo Code
  • Zencoder
  • Goose
  • Void
  • Augment Code
  • Amp
  • Qodo
  • Tabnine
  • Pieces
  • Sourcegraph Cody
  • JetBrains
  • Warp
  • Amazon Q
  • Antigravity
  • BoltAI
  • Raycast
  • Jan
  • LM Studio
  • AnythingLLM
  • Open WebUI
  • Msty
  • Cherry Studio
  • LibreChat
  • TypingMind
  • Chorus
  • 5ire
  • n8n
  • LangChain
  • LlamaIndex
  • CrewAI
  • Vercel AI SDK

Before you connect

Questions about Pinecone.

The practical details behind the request, access and result.

Can I use the Pinecone MCP to manage my vector database?

Yes, this Connector connects your AI client directly to Pinecone. You can query data, check stats, and manage your indexes using natural language.

How do I check my vector capacity with Pinecone?

You can simply ask your agent to pull the usage stats. It will check your pod capacity and vector counts in real time.

Can my AI agent delete vectors for me?

Yes, you can give your agent the command to delete specific vectors. This is great for clearing out test data or handling user deletion requests.

Is the Pinecone MCP good for testing RAG?

It's perfect for RAG. You can ask your agent to run queries and see what context it retrieves, helping you debug relevance without writing scripts.

How do I see all my indexes in Pinecone?

Just ask your agent to list your indexes. It will return a list of all the indexes currently in your environment.

Can I use this to check my index configurations?

Yes, your agent can describe any specific index to show you its configuration, topology, and other parameters instantly.

Can the AI execute raw vector similarity searches?

Yes, absolutely. Once you supply the raw semantic embedding coordinates (normally a float array generated previously), the LLM can funnel it through the query_vectors capability. The Pinecone DB will process this and return the top-K closest vector matches along with embedded metadata.

How do I check my remaining vector storage capacity?

It's extremely simple. Just ask the connected AI agent to 'Get the index stats'. It will internally call get_index_stats against the specified index namespace, returning total vector count and physical dimensionality limits to your chat window.

Is it safe to delete vectors dynamically using the chat terminal?

Yes, but with standard precautions. The delete_vectors capability operates exactly as the official SDK. As long as you maintain clear contextual scopes and ID filtering in your prompts, the execution is purely deterministic and secure.

One connection away

Give your agent a direct line to Pinecone.

Connect Pinecone once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available