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Connect Pinecone to ChatGPT, Claude and Gemini

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Manage your embeddings and index health through natural language.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

A futuristic MCP client docking bay
V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 7 capabilities

We ship only the actions that matter.

Capability set01 / 02

01-04

4 capabilities in this set.

Part of 7 available through Pinecone.

  1. 01

    List indexes

    This capability shows you every Pinecone index currently in your account.

  2. 02

    Query vectors

    Use this to find the most similar vectors and their associated metadata based on your input.

  3. 03

    Delete vectors

    This capability removes specific vectors from an index to clean up your data.

  4. 04

    Describe index

    Get the specific configuration and setup details for a particular index.

Capability set02 / 02

05-07

3 capabilities in this set.

Part of 7 available through Pinecone.

  1. 05

    Fetch vectors

    This capability pulls specific vectors from your index using their unique IDs.

  2. 06

    Get index stats

    Get real-time usage statistics and health metrics for your index.

  3. 07

    List collections

    This capability returns a list of all index collections available in your environment.

Real work

What teams actually run

01

RAG Debugging

Verify why certain documents aren't being retrieved by querying vectors directly in chat.

02

Database Auditing

Check index stats to ensure your vector count and pod usage stay within limits.

03

Data Cleanup

Remove outdated or incorrect embeddings using targeted delete commands.

04

Environment Management

Quickly list and inspect all active indexes and collections in your account.

Automate a task

Put Pinecone to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

Pinecone works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put Pinecone to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

933ms average. Fast in production.

Pinecone is checked daily against the live service.

Daily averagePeak 1043ms
Oct 3Today
Fastest day
793ms
Slowest day
1043ms
14-day trend
Stable0%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 7 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Pinecone, so you can see the experience inside your AI.

It does not authenticate your account with Pinecone. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Pinecone Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_cMLbuhs0rnfwH86edlO4MVqnOLr94ybdUvJavFsC/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Pinecone capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "pinecone-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_cMLbuhs0rnfwH86edlO4MVqnOLr94ybdUvJavFsC/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Pinecone in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Pinecone owners hand off.

This MCP is built for technical users who need to interact with vector data without writing boilerplate code.

  • 01

    AI/ML Engineers

    They use the agent to troubleshoot the relevance of semantic chunks during RAG testing.

  • 02

    Data Custodians

    They audit storage capacity and verify that garbage collection worked correctly.

  • 03

    Agent Builders

    They test Pinecone core endpoints directly within a workspace like Cursor.

FAQ

Questions Pinecone owners ask.

  • 01

    How do I connect my Pinecone account?

    You subscribe to the MCP on Vinkius and enter your Pinecone API key. Once connected, your AI client can immediately start using the capabilities.

  • 02

    Which AI clients can use this MCP?

    This MCP works with any compatible client, including Claude, Cursor, Windsurf, and VS Code.

  • 03

    Can I delete data using this MCP?

    Yes, you can use the delete_vectors capability to remove specific embeddings from your index via your agent.

  • 04

    Is it possible to check my index usage?

    Yes, the get_index_stats capability allows your agent to pull real-time health and capacity metrics.

  • 05

    Do I need to write code to query my vectors?

    No, you can use natural language to tell your agent to perform similarity searches using the query_vectors capability.

  • 06

    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.

  • 07

    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.

  • 08

    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.

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