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Mistralai MCP Integration for Cursor
Connect Mistralai to Cursor through the local KosmoKrator MCP gateway with scoped tools, credentials, and write policy.
Connect Mistralai to Cursor
Expose selected local integrations to Cursor through KosmoKrator without configuring each service as its own MCP server.
Create or update .cursor/mcp.json with a KosmoKrator stdio server entry. The gateway is local, scoped to this integration, and starts with
--write=deny so Cursor can inspect read-capable tools without receiving write access by default.
Mistralai MCP Config for Cursor
Use the same KosmoKrator install and integration credentials that power terminal and headless runs.
{
"mcpServers": {
"kosmokrator-mistralai": {
"type": "stdio",
"command": "kosmo",
"args": [
"mcp:serve",
"--integration=mistralai",
"--write=deny"
]
}
}
} Run the Gateway Manually
kosmokrator mcp:serve --integration=mistralai --write=deny Why Use KosmoKrator Here
Expose only Mistralai instead of a broad multi-service tool list.
Reuse credentials already configured for the KosmoKrator CLI and Lua runtime.
Start read-only, then opt into ask or allow for trusted workspaces.
Mistralai Tools Visible to Cursor
Cursor sees stable MCP tool names generated from the Mistralai integration catalog.
| MCP tool | Source function | Type | Description |
|---|---|---|---|
integration__mistralai__mistralai_chat | mistralai.mistralai_chat | Write | Generate a chat completion using a MistralAI model. Send a list of messages (with roles "system", "user", or "assistant") and receive a model-generated response. Use temperature to control creativity (0 = deterministic, 1 = creative). |
integration__mistralai__mistralai_create_embedding | mistralai.mistralai_create_embedding | Write | Generate text embeddings using a MistralAI embedding model. Converts text into numerical vectors for semantic search, similarity comparison, or clustering. Supports single strings or arrays of strings. |
integration__mistralai__mistralai_list_models | mistralai.mistralai_list_models | Read | List all models available in your MistralAI account. Returns model IDs, creation timestamps, and capabilities. Use this to discover which models you can use for chat completions or embeddings. |
integration__mistralai__mistralai_finetune | mistralai.mistralai_finetune | Write | Create a fine-tuning job on MistralAI. Upload training data and select a base model to create a custom fine-tuned model. The job runs asynchronously — check the returned job ID for status updates. |
integration__mistralai__mistralai_list_agents | mistralai.mistralai_list_agents | Read | List all MistralAI agents in your account. Returns agent IDs, names, models, and descriptions. Agents are AI assistants with custom instructions and tools. |
integration__mistralai__mistralai_create_agent | mistralai.mistralai_create_agent | Write | Create a new MistralAI agent. Specify a name, model, and instructions to define how the agent should behave. Agents are persistent AI assistants that can be used for conversations with custom personalities and capabilities. |
integration__mistralai__mistralai_get_current_user | mistralai.mistralai_get_current_user | Read | Get information about the currently authenticated MistralAI user. Returns account details, subscription tier, and usage information. |