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Cohere MCP Server for Cursor 6 tools — connect in under 2 minutes

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Cursor is an AI-first code editor built on VS Code that integrates LLM-powered coding assistance directly into the development workflow. Its Agent mode enables autonomous multi-step coding tasks, and MCP support lets agents access external data sources and APIs during code generation.

Vinkius supports streamable HTTP and SSE.

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Classic Setup·json
{
  "mcpServers": {
    "cohere": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}
Cohere
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High SecurityEnterprise-grade
IAMAccess control
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DLPData protection
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<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Cohere MCP Server

Connect your Cohere account to any AI agent and leverage enterprise-grade AI models through natural conversation.

Cursor's Agent mode turns Cohere into an in-editor superpower. Ask Cursor to generate code using live data from Cohere and it fetches, processes, and writes. all in a single agentic loop. 6 tools appear alongside file editing and terminal access, creating a unified development environment grounded in real-time information.

What you can do

  • Model Discovery — List all available Cohere models with their names, capabilities and context lengths
  • Chat API — Send conversations to Command models (command-r-plus, command-r, command-r7b) and receive responses with citations and tool call support
  • Embeddings — Generate vector embeddings for semantic search with multiple embedding types (float, int8, uint8, binary)
  • Reranking — Rerank documents by relevance to a search query using Cohere's industry-leading reranking models
  • Tokenization — Tokenize and detokenize text for estimating token counts and debugging

The Cohere MCP Server exposes 6 tools through the Vinkius. Connect it to Cursor in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Cohere to Cursor via MCP

Follow these steps to integrate the Cohere MCP Server with Cursor.

01

Open MCP Settings

Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"

02

Add the server config

Paste the JSON configuration above into the mcp.json file that opens

03

Save the file

Cursor will automatically detect the new MCP server

04

Start using Cohere

Open Agent mode in chat and ask: "Using Cohere, help me...". 6 tools available

Why Use Cursor with the Cohere MCP Server

Cursor AI Code Editor provides unique advantages when paired with Cohere through the Model Context Protocol.

01

Agent mode turns Cursor into an autonomous coding assistant that can read files, run commands, and call MCP tools without switching context

02

Cursor's Composer feature can generate entire files using real-time data fetched through MCP. no copy-pasting from external dashboards

03

MCP tools appear alongside built-in tools like file reading and terminal access, creating a unified agentic environment

04

VS Code extension compatibility means your existing workflow, keybindings, and extensions all work alongside MCP tools

Cohere + Cursor Use Cases

Practical scenarios where Cursor combined with the Cohere MCP Server delivers measurable value.

01

Code generation with live data: ask Cursor to generate a security report module using live DNS and subdomain data fetched through MCP

02

Automated documentation: have Cursor query your API's tool schemas and generate TypeScript interfaces or OpenAPI specs automatically

03

Infrastructure-as-code: Cursor can fetch domain configurations and generate corresponding Terraform or CloudFormation templates

04

Test scaffolding: ask Cursor to pull real API responses via MCP and generate unit test fixtures from actual data

Cohere MCP Tools for Cursor (6)

These 6 tools become available when you connect Cohere to Cursor via MCP:

01

chat

Requires the model ID (e.g. "command-r-plus", "command-r", "command-r7b") and messages array in JSON format. Each message must have a "role" ("user", "assistant", "system" or "tool") and "content" (text or array of content blocks). Optionally set max_tokens, temperature (0-1), p (nucleus sampling 0-1) and tools array for function calling. Returns the model's response with text, citations and tool calls. Send a chat message to a Cohere model

02

detokenize

Requires the token IDs array. Returns the reconstructed text. Useful for debugging and verifying tokenization. Detokenize token IDs back to text using Cohere

03

embed

Requires the model ID (e.g. "embed-v4", "embed-v3"), texts array and input_type ("search_document", "search_query", "classification", "clustering"). Returns embedding vectors for each input text. Useful for semantic search, similarity comparison and vector database storage. Generate embeddings using Cohere

04

list_models

Each model returns its name (e.g. "command-r-plus", "command-r", "embed-v4", "rerank-v3.5"), endpoint compatibility, context length and tokenization info. Use this to discover which models are available and their capabilities. List all available Cohere models

05

rerank

Requires the model ID (e.g. "rerank-v3.5", "rerank-english-v3.0"), query text and documents array. Optionally set top_n to return only the top N results. Returns ranked documents with relevance scores. Rerank documents by relevance to a query

06

tokenize

Requires the text to tokenize and optionally the model. Returns the list of token IDs and token strings. Useful for estimating token counts before sending to chat or embed endpoints. Tokenize text using Cohere

Example Prompts for Cohere in Cursor

Ready-to-use prompts you can give your Cursor agent to start working with Cohere immediately.

01

"Send a message to Command R+ asking 'What is the capital of Brazil?'"

02

"Rerank these documents for the query 'machine learning models': ['Neural networks are inspired by biological neurons.', 'Python is a popular programming language.', 'Transformers use attention mechanisms for sequence processing.']"

03

"Generate embeddings for these texts: ['The weather is nice today.', 'I love programming in Python.'] using embed-v4."

Troubleshooting Cohere MCP Server with Cursor

Common issues when connecting Cohere to Cursor through the Vinkius, and how to resolve them.

01

Tools not appearing in Cursor

Ensure you are in Agent mode (not Ask mode). MCP tools only work in Agent mode.
02

Server shows as disconnected

Check Settings → Features → MCP and verify the server status. Try clicking the refresh button.

Cohere + Cursor FAQ

Common questions about integrating Cohere MCP Server with Cursor.

01

What is Agent mode and why does it matter for MCP?

Agent mode is Cursor's autonomous execution mode where the AI can perform multi-step tasks: reading files, editing code, running terminal commands, and calling MCP tools. Without Agent mode, Cursor operates in a simpler ask-and-answer mode that doesn't support tool calling. Always ensure you're in Agent mode when working with MCP servers.
02

Where does Cursor store MCP configuration?

Cursor looks for MCP server configurations in a mcp.json file. You can configure servers at the project level (.cursor/mcp.json in your project root) or globally (~/.cursor/mcp.json). Project-level configs take precedence.
03

Can Cursor use MCP tools in inline edits?

No. MCP tools are only available in Agent mode through the chat panel. Inline completions and Tab suggestions do not trigger MCP tool calls. This is by design. tool calls require user visibility and approval.
04

How do I verify MCP tools are loaded?

Open Settings → Features → MCP and look for your server name. A green indicator means the server is connected. You can also check Agent mode's available tools by clicking the tools dropdown in the chat panel.

Connect Cohere to Cursor

Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.