Cohere Connector for AI agents.
6 live capabilities
Connect your agent to enterprise-grade reranking and embeddings.
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Why people use Cohere
Solving the Messy Search Problem with Cohere
This Connector changes the game. Your agent can now call rerank to sort those results by actual relevance scores before it even sees them. You get a clean, prioritized list of facts, which means your agent gives much better answers.
What Vinkius changes
You get a direct pipeline to Cohere's models without writing a single line of HTTP code.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Refining a RAG pipeline
A developer asks the agent to rerank 50 search results to find the top 5 most relevant facts.
- Real-world use case 02
Building a semantic search
An engineer uses embed to turn a product catalog into vectors for a find similar items feature.
- Real-world use case 03
Multi-model orchestration
A user asks the agent to list_models to see which Command version has the best context for a long document.
Complete set · 6capabilities
The complete Cohere capability set.
These are the exact actions your AI can choose when you ask it to work with Cohere.
01—03
3 capabilities in this set.
Part of 6 available through Cohere.
- 01 Capability
Detokenize
Convert a list of token IDs back into plain text for debugging and verification. This helps you see exactly what your agent is processing.
- 02 Capability
Chat
Send a message to a Cohere Command model to get a response with citations and capability call support. This is useful for high-quality conversational tasks.
- 03 Capability
List models
See a full list of Cohere models, their capabilities, and their context lengths. Use this to discover which models fit your specific needs.
04—06
3 capabilities in this set.
Part of 6 available through Cohere.
- 04 Capability
Rerank
Reorder a list of documents based on how well they match a specific search query. This ensures your agent gets the most relevant facts first.
- 05 Capability
Tokenize
Break down text into token IDs to estimate costs and limits before sending data. This helps you manage your token budget effectively.
- 06 Capability
Embed
Create vector embeddings for various tasks like search, classification, or clustering. This is the standard way to prepare data for a vector database.
Set up in minutes
One URL. Then ask Cohere to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Cohere from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_R4ERIik35jE1vo5hRbvNHBrwGzLbWOtl3sZQNmPU/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Cohere, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Cohere for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_R4ERIik35jE1vo5hRbvNHBrwGzLbWOtl3sZQNmPU/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Cohere URL.
- Step 03
Save and start
Save the connection and enable Cohere in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"cohere": {
"url": "https://edge.vinkius.com/vk_preview_R4ERIik35jE1vo5hRbvNHBrwGzLbWOtl3sZQNmPU/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Cohere
Open Agent mode in chat and ask: "Using Cohere, help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"cohere": {
"url": "https://edge.vinkius.com/vk_preview_R4ERIik35jE1vo5hRbvNHBrwGzLbWOtl3sZQNmPU/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Cohere
Ask Copilot: "Using Cohere, help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"cohere": {
"url": "https://edge.vinkius.com/vk_preview_R4ERIik35jE1vo5hRbvNHBrwGzLbWOtl3sZQNmPU/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Cohere
Open Cascade and ask: "Using Cohere, help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"cohere": {
"url": "https://edge.vinkius.com/vk_preview_R4ERIik35jE1vo5hRbvNHBrwGzLbWOtl3sZQNmPU/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Cohere
Ask Cline: "Using Cohere, help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add cohere --transport http "https://edge.vinkius.com/vk_preview_R4ERIik35jE1vo5hRbvNHBrwGzLbWOtl3sZQNmPU/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Cohere
Ask Claude: "Using Cohere, show me...". 6 tools are ready
Where the request belongs
Work Cohere can move forward.
For the ML engineer tired of boilerplate, the search dev building a RAG pipeline, or the dev who needs high-quality reranking without the headache of manual scoring.
ML Engineer
Builds production search systems by generating embeddings and reranking results.
Search Engineer
Refines RAG pipelines to ensure the agent only sees the most relevant information first.
Backend Developer
Integrates Command models into apps without managing complex API requests.
When one Connector is not enough
Carry the request into a workflow.
Combine Cohere with the systems that finish the task.
View all recipesBuild the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsCohere (AI Platform)
Power enterprise AI via Cohere. generate text, perform chat completions, reorder documents, and manage embeddings directly from any AI agent.
Gradient AI (LLM API & Finetuning)
Access powerful LLMs, fine-tune models on your own data, and generate embeddings directly through your AI agent.
Cohere (Embed & Rerank)
Empower RAG via Cohere. generate high-quality text embeddings, rerank documents for better accuracy, and perform AI classification directly from any AI agent.
Mistral AI (Frontier LLMs & Embeddings)
Manage AI inference via Mistral. execute chat completions, generate RAG embeddings, and audit frontier models.
Voyage AI (AI Embeddings API)
Generate high-quality text, multimodal, and contextualized embeddings, plus high-precision reranking for RAG workflows.
Eden AI
Access 100+ AI models through a single API. route LLMs, generate embeddings, and execute specialized AI tasks like OCR and translation.
Bring your own AI
Change the model, client or framework. Keep Cohere 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 Cohere.
The practical details behind the request, access and result.
What does the Cohere MCP do for my AI agent?
It gives your agent the ability to use Cohere's models for reranking, embeddings, and chat. It acts as a bridge so your agent can perform complex NLP tasks without needing custom code.
Can I use Cohere MCP to improve my search results?
Yes, it's great for that. You can use it to rerank a list of documents, ensuring your agent only sees the most relevant information first.
How do I get embeddings for my database using Cohere?
You can have your agent use the embed capability to turn your text into vectors. This works for search, clustering, and classification tasks.
Does the Cohere MCP support the Command models?
Yes, it lets your agent send messages to Command-R, Command-R+, and Command-R7B to get high-quality, cited responses.
Can I check my token counts with Cohere MCP?
Yes, you can use the tokenize capability to see exactly how many tokens a piece of text uses before you send it to a model.
Is Cohere MCP good for RAG systems?
It's a top choice for RAG because it handles the two hardest parts: generating high-quality embeddings and reranking the retrieved results.
How do I get a Cohere API Key?
Log in to the Cohere Dashboard, go to API Keys and click Create API Key. Copy the key immediately. it starts with a random string and won't be shown again. Free tier includes trial access with rate limits.
What models are available?
Use the list_models capability to see all available Cohere models. Key models include command-r-plus (most capable, 128K context), command-r (efficient, 128K context), command-r7b (lightweight, 128K context), embed-v4 (embeddings) and rerank-v3.5 (reranking).
Can I send multi-turn conversations?
Yes! Pass a messages array with alternating 'user', 'assistant' and 'system' roles. Each message has a 'role' and 'content' field. Command models support function calling and will return tool_calls when appropriate.
What is reranking and when should I use it?
Reranking reorders a set of documents by their relevance to a query. Use it after an initial search to improve result quality. The rerank capability takes a query, list of documents and returns them ranked by relevance score. Cohere's rerank models are industry-leading for search applications.
One connection away
Give your agent a direct line to Cohere.
Connect Cohere once. Keep it beside 5,900+ managed Connectors when the next task needs more.
Explore every Connector No credit card required · Free tier available