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Mistralai MCP Integration for Codex
Connect Mistralai to Codex through the local KosmoKrator MCP gateway with scoped tools, credentials, and write policy.
Connect Mistralai to Codex
Use KosmoKrator as a local MCP proxy for Codex so coding sessions can reach selected integrations with explicit write policy.
Register kosmo mcp:serve as a local stdio server and choose the integration allowlist. The gateway is local, scoped to this integration, and starts with
--write=deny so Codex can inspect read-capable tools without receiving write access by default.
Mistralai MCP Config for Codex
Keep write access denied or ask-based unless the workspace is trusted.
{
"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 Codex
Codex 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. |