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Connect Cohere AI Platform 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. Run advanced NLP workflows directly from your AI client.

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 · 5 capabilities

We ship only the actions that matter.

Capability set01 / 02

01-03

3 capabilities in this set.

Part of 5 available through Cohere.

  1. 01

    Rerank documents

    This capability reorders a list of documents based on how well they actually match a specific query. It helps you fix low-quality search results by prioritizing semantic relevance.

  2. 02

    Generate embeddings

    This capability creates dense vector representations from plain text. You'll use it to power semantic search or similarity matching in your applications.

  3. 03

    Chat completion

    Use this to generate conversational text responses. It lets your agent call Cohere's chat models to handle complex dialogue or text transformations.

Capability set02 / 02

04-05

2 capabilities in this set.

Part of 5 available through Cohere.

  1. 04

    Tokenize text

    This capability breaks text down into specific integer segments. It uses the token dictionaries tied to specific Cohere encoding models.

  2. 05

    List models

    This capability pulls a list of all Cohere models available under your current plan. It's how you verify which model identifiers your agent can actually use.

Real work

What teams actually run

01

RAG Optimization

Use reranking capabilities to ensure your agent retrieves the most relevant context for its answers.

02

Semantic Search Development

Generate embeddings for large datasets to build similarity-based search engines.

03

Rapid Prototyping

Product teams can test generative features using enterprise models without writing custom integration code.

04

NLP Debugging

Engineers can inspect tokenization and model capabilities to troubleshoot application errors.

Automate a task

Put Cohere 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

Cohere 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 Cohere 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

887ms average. Fast in production.

Cohere is checked daily against the live service.

Daily averagePeak 1010ms
Oct 3Today
Fastest day
647ms
Slowest day
1010ms
14-day trend
Slowing+15%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 5 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 Cohere, so you can see the experience inside your AI.

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

Cohere Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_fA1l2oqJxpD7QL48YxxEDHfZLGLU7FMkmFVJY7Ft/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 — Cohere capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "cohere-ai-platform-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_fA1l2oqJxpD7QL48YxxEDHfZLGLU7FMkmFVJY7Ft/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 Cohere in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Cohere owners hand off.

This MCP is built for technical teams working on production-grade NLP and generative AI applications.

  • 01

    AI Developers

    They use the MCP to test and debug chat completion and text generation logic through natural language.

  • 02

    Data Scientists

    They use it to evaluate embedding quality and reranking performance for RAG pipelines in real-time.

  • 03

    Engineers

    They use the capabilities to audit tokenization and verify model availability for complex NLP boundaries.

FAQ

Questions Cohere owners ask.

  • 01

    What AI clients can I use with this Cohere MCP?

    You can use this MCP with any compatible client, including Claude, Cursor, Windsurf, and VS Code.

  • 02

    Do I need my own Cohere API key?

    Yes, you need to provide your own Cohere API key, whether it is a Trial or Production key from your Cohere dashboard.

  • 03

    Can I use this to improve my RAG accuracy?

    Yes, you can use the rerank_documents capability to reorder search results by semantic relevance, which improves the context provided to your agent.

  • 04

    How do I know which models I can access?

    You can use the list_models capability to see all available model identifiers and hashes permitted by your specific Cohere plan.

  • 05

    Is this MCP hosted?

    Yes, Vinkius hosts and manages the MCP, so you don't have to worry about the underlying infrastructure or keeping it running.

  • 06

    Can my agent use Cohere to generate creative or technical text?

    Yes. The 'generate_text' and 'chat_generation' capabilities allow you to leverage Cohere's Command models. You can provide prompts for anything from copywriting to code generation, and the agent will return the synthesized token strings.

  • 07

    How do I perform high-dimensional vector searches with Cohere?

    Use the 'generate_embeddings' capability. Provide an array of texts, and your agent will return the precise dense vector shapes (floats). These can then be stored in a vector database like Chroma or ClickHouse for similarity matching.

  • 08

    Can I audit token usage before sending a long prompt?

    Absolutely. The 'tokenize_text' capability retrieves the exact structural segmentation of your text based on the specific model's dictionary. This allows you to verify token counts and manage your context window limits efficiently.

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