How to Use the Cohere MCP in Cursor

Q: How do I add the Cohere MCP Server to my Cursor project?

Create an mcp.json file inside your project's .cursor directory and define the server. Once saved, enable Agent mode in the Cursor chat pane to start calling the tools.

Q: Can Cursor use Cohere to rank my project search results?

Yes, you can use the rerank tool to sort code snippets or markdown files by relevance. Your Cursor agent calls this tool to identify which files are most important to your active coding task.

Q: How do I generate embeddings inside Cursor using Cohere?

The agent invokes the embed tool on your selected workspace files or text arrays. It returns the precise vector values, which the agent can then write directly into your local database seed files.

Q: Can I control the temperature of the chat tool?

Yes, the chat tool accepts optional parameters like temperature and nucleus sampling. You can tell your editor agent to adjust these values to get more deterministic code outputs.

Write code in Cursor using live Cohere embeddings, document ranking, and generation tools directly from your agent.

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Connect Cohere MCP to Cursor

Create your Vinkius account to connect Cohere to Cursor and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cohere with Cursor

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Generate production code using the Cohere MCP Server

The `chat` tool lets your Cursor agent query Cohere Command models to write, refactor, and debug code based on live model responses. Instead of pasting static examples, the editor agent pulls fresh Cohere schema details and model citations directly into your active Cursor file. You can verify active Cohere capabilities using `list_models` to ensure your Cursor editor is targeting the correct context window size. This integration operates directly within Cursor's Agent mode using your local `.cursor/mcp.json` configuration.

Build semantic search features with real-time embeddings

The `embed` tool generates high-dimensional vector representations of your workspace files using Cohere's embed-v4 model directly inside Cursor. The Cursor agent uses these vectors to write accurate similarity search logic that matches the exact dimensions of Cohere's embedding space. Having the actual Cohere vector outputs available in Cursor means your agent can write real test assertions instead of guessing array lengths. It bridges the gap between writing code in Cursor and testing actual Cohere model outputs.

Optimize context windows using tokenization tools

The `tokenize` tool counts exact token IDs for any code snippet or text block you select in Cursor using Cohere's specific tokenizer. When you need to verify how Cursor parses a string, the `detokenize` tool translates those Cohere IDs back to raw text in your editor. This MCP integration helps you write precise buffer management code for your own Cohere-powered applications. You can test how different Cohere models split your Cursor code syntax without leaving your editor.

Setup guide

Set up Cohere MCP in Cursor

Prerequisites

Cursor installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP Settings

Go to Cursor Settings → MCP or open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and search for "MCP: Add Server".
2

Add the Cohere MCP

Cursor will create or open .cursor/mcp.json in your project root. Paste the JSON snippet on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Enable Agent mode

Open Composer (Cmd+I / Ctrl+I) and switch to Agent mode using the dropdown at the top. MCP tools are only available in Agent mode.
4

Verify the connection

Ask Cursor something like "List my recent Cohere transactions." If the MCP tools are loaded correctly, Cursor will call the Cohere tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

{
  "mcpServers": {
    "cohere-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cohere. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Cohere MCP in Cursor

Create an `mcp.json` file inside your project's `.cursor` directory and define the server. Once saved, enable Agent mode in the Cursor chat pane to start calling the tools.

Yes, you can use the `rerank` tool to sort code snippets or markdown files by relevance. Your Cursor agent calls this tool to identify which files are most important to your active coding task.

The agent invokes the `embed` tool on your selected workspace files or text arrays. It returns the precise vector values, which the agent can then write directly into your local database seed files.

Yes, the `chat` tool accepts optional parameters like temperature and nucleus sampling. You can tell your editor agent to adjust these values to get more deterministic code outputs.

Only the specific code files, text blocks, or queries you explicitly pass to tools like `chat` or `rerank` leave your machine. These payloads travel through encrypted, short-lived Vinkius tunnels directly to the model endpoints, bypassing persistent storage entirely.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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