other
Mistralai CLI for Shell Scripts
Use the Mistralai CLI for shell scripts with headless JSON commands, schema discovery, credentials, and permission controls.
7 functions 3 read 4 write API key auth
Mistralai CLI for Shell Scripts
Call integration functions from shell scripts with stable JSON input and output.
Use shell scripts for small local automations that need one or more integration calls. The Mistralai CLI uses the same integration registry as the TUI, Lua runtime, and MCP gateway, but returns predictable command output for automation.
Command Shape
# Mistralai CLI for Shell Scripts
kosmokrator integrations:configure mistralai --set api_key="$MISTRALAI_API_KEY" --enable --read allow --write ask --json
kosmo integrations:call mistralai.mistralai_chat '{"model":"example_model","messages":"example_messages","temperature":1,"max_tokens":1}' --jsonDiscovery Before Execution
Agents and scripts can inspect Mistralai docs and schemas before choosing a function.
kosmo integrations:docs mistralai --json
kosmo integrations:docs mistralai.mistralai_chat --json
kosmo integrations:schema mistralai.mistralai_chat --json
kosmo integrations:search "Mistralai" --json
kosmo integrations:list --jsonUseful Mistralai CLI Functions
| Function | Type | Parameters | Description |
|---|---|---|---|
mistralai.mistralai_chat | Write | model, messages, temperature, max_tokens | 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). |
mistralai.mistralai_create_embedding | Write | model, input | 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. |
mistralai.mistralai_list_models | Read | none | 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. |
mistralai.mistralai_finetune | Write | model, training_files, hyperparameters, suffix | 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. |
mistralai.mistralai_list_agents | Read | none | List all MistralAI agents in your account. Returns agent IDs, names, models, and descriptions. Agents are AI assistants with custom instructions and tools. |
mistralai.mistralai_create_agent | Write | name, model, instructions, description | 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. |
mistralai.mistralai_get_current_user | Read | none | Get information about the currently authenticated MistralAI user. Returns account details, subscription tier, and usage information. |
Automation Notes
- Use
--jsonfor machine-readable output. - Keep credentials out of argv by using environment variables or stored KosmoKrator configuration.
- Configure read/write policy before unattended runs; use
--forceonly for trusted automation. - Use the MCP gateway instead when the agent needs dynamic tool discovery inside a conversation.