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Mistralai MCP Integration for CrewAI
Connect Mistralai to CrewAI through the local KosmoKrator MCP gateway with scoped tools, credentials, and write policy.
Connect Mistralai to CrewAI
Expose KosmoKrator integrations to CrewAI workers as scoped local tools.
Wrap kosmo integrations:call for specific tasks or connect workers to a local MCP gateway. The gateway is local, scoped to this integration, and starts with
--write=deny so CrewAI can inspect read-capable tools without receiving write access by default.
Mistralai MCP Config for CrewAI
Use per-worker integration scopes to avoid giving every worker every tool.
{
"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 CrewAI
CrewAI 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. |