# Mistral AI MCP

> Mistral AI connects your agent to a full suite of state-of-the-art language model capabilities. You can run complex conversational tasks, generate dense text embeddings for search, or perform specialized code completions like Fill-in-the-Middle (FIM). It also allows you to audit available models and trigger custom multi-step AI workflows.

## Overview
- **Category:** ai-frontier
- **Price:** Free
- **Tags:** llm, inference, rag, embeddings, natural-language-processing, model-api

## Description

This MCP lets your agent interact with Mistral's advanced model suite without needing complex SDK setup. You get full control over running different types of inference, whether it’s general chat or highly specialized tasks like code completion. Need to power a semantic search? Use the embedded tools to calculate vector representations from any text block. For building autonomous systems, you can trigger custom multi-step workflows and even check content against safety policies before deployment. If your current development stack uses various API keys for different providers, Vinkius brings all these advanced Mistral capabilities together into one place. You connect once through the Vinkius catalog and immediately gain access to this comprehensive set of tools.

## Tools

### chat_completion
Runs conversational inference using Mistral AI's chat completion models for structured text output.

### generate_embeddings
Calculates numerical vectors from provided text data using a dedicated embedding model.

### list_models
Retrieves an inventory of all currently available Mistral AI models that the client can use.

### get_model
Fetches specific details and metadata about one particular Mistral AI model ID.

### fim_completion
Generates missing code logic by filling in the gap between a defined prefix and suffix of code.

### moderate_content
Checks user-provided content against safety rules to ensure compliance before processing or deployment.

### agent_completion
Initiates and manages a custom, multi-step autonomous agent workflow defined by Mistral AI.

## Prompt Examples

**Prompt:** 
```
Run a chat completion using 'mistral-large-latest' to summarize this research paper: [text]
```

**Response:** 
```
Executing conversational inference… I've processed the text using Mistral Large. Here is a 3-point summary: 1) The study identifies a 15% efficiency gain in transformers, 2) Data centers show reduced latency with the new protocol, and 3) Results are consistent across test sets. Would you like a more detailed breakdown?
```

**Prompt:** 
```
Generate code to complete this gap: Prefix 'def calculate_fib(n):', Suffix 'return sequence'
```

**Response:** 
```
Executing Codestral FIM completion… I've generated the missing logic: 'sequence = [0, 1]; for i in range(2, n): sequence.append(sequence[i-1] + sequence[i-2])'. This perfectly bridges your prefix and suffix. Would you like me to generate unit tests for this function?
```

**Prompt:** 
```
List all available Mistral models and their IDs
```

**Response:** 
```
I've retrieved the Mistral model inventory. Highlights include 'mistral-large-latest' (General purpose), 'mistral-small-latest' (Fast inference), 'codestral-latest' (Coding), 'pixtral-12b-2409' (Multimodal), and 'mistral-embed' (Embeddings). Which model would you like to inspect further?
```

## Capabilities

### Run Conversational Inference
Execute high-fidelity chat completions using Mistral's various models, giving you detailed control over system instructions and message history.

### Calculate Text Embeddings
Generate dense numerical vectors for any text. This powers semantic search engines and knowledge retrieval systems.

### Complete Code Logic (FIM)
Fill in missing sections of code, bridging the logical gap between existing prefixes and required suffixes.

### Execute Agent Workflows
Trigger multi-step, autonomous agent processes that handle complex reasoning tasks on your behalf.

### Inspect Model Metadata
List all available Mistral AI models and retrieve detailed configuration settings to determine the best model for a job.

## Use Cases

### Building a Document Search Portal
A data scientist needs to index 10,000 legal documents. They use generate_embeddings to convert all text into vectors and then pass the list of model IDs to get_model, confirming that the embedding process is using the correct, stable version.

### Creating a Code Copilot Feature
A developer wants an in-IDE assistant. They use fim_completion when they type 'def calculate_fib(n):' and only need to write the closing bracket; the tool fills in all the complex loop logic.

### Testing Agent Logic Flow
A researcher wants a multi-step agent to analyze market sentiment. They call agent_completion, which runs a sequence of reasoning steps and returns a final structured report without the developer needing to write orchestration code.

### Pre-deployment Content Scrubbing
A content team uploads user reviews that might contain prohibited material. Before storing them, they use moderate_content to run safety checks on every single entry, rejecting anything that fails the compliance filter.

## Benefits

- Deep control over model selection. Use list_models to compare different Mistral variants and get the exact metadata you need before running chat_completion.
- Build high-performance search features instantly. The generate_embeddings tool lets your agent convert any text into searchable vectors, making RAG pipelines easy.
- Improve code quality with FIM. Instead of generic auto-complete, fim_completion fills in logical gaps, requiring you to provide only the start and end points.
- Manage complexity through automation. You don't write multi-step API calls; you just call agent_completion to run a sophisticated workflow.
- Ensure safety compliance upfront. moderate_content checks inputs against toxicity policies, giving you confidence that the content is safe before it hits production.

## How It Works

The bottom line is you talk to your AI client normally, but it uses this MCP to handle all the complex model calls and data processing behind the scenes.

1. Subscribe to this MCP on Vinkius and enter your unique Mistral AI API Key.
2. Select your preferred connection point, like Claude or Cursor, and activate the Mistral tools within your agent's context.
3. Call a specific tool—for example, generate_embeddings—and pass the required text data to get immediate results.

## Frequently Asked Questions

**How do I use Mistral AI (Frontier LLMs & Embeddings) for semantic search?**
You calculate dense numerical vectors using the generate_embeddings tool. This process converts raw text into a vector representation that powers your semantic search database.

**Can I use Mistral AI (Frontier LLMs & Embeddings) for code filling?**
Yes, you use fim_completion. You provide the existing code prefix and suffix, and the tool generates the missing logic in between.

**What is the purpose of list_models with Mistral AI (Frontier LLMs & Embeddings)?**
list_models provides an inventory of all active Mistral models. This helps you identify which model ID to use for a specific task, like choosing between 'mistral-large' and 'mistral-small'.

**Does Mistral AI (Frontier LLMs & Embeddings) handle safety checks?**
Yes, you can run moderate_content. This tool runs the content through rigorous toxicity policies to verify compliance before you deploy or store it.

**Is chat_completion better than agent_completion for complex tasks?**
No. Use chat_completion for single-turn conversations. If a task requires multiple steps of reasoning, calling agent_completion is the correct method for autonomous execution.