mistral-mcp
Related Servers
Alternatives to mistral-mcp
No user-submitted related servers found.
Related Servers
- AlicenseDqualityDmaintenanceA TypeScript MCP server demo supporting local Stdio and remote Streamable HTTP, demonstrating tool invocation for AI agents.2MIT
- AlicenseNot gradedqualityAmaintenanceTypeScript AI SDK with a built-in MCP client: 58+ MCP servers over 4 transports (stdio, HTTP, SSE, WebSocket), 24+ LLM providers behind one interface, streaming, tool calling, RAG, voice (TTS/STT/realtime), and task scheduling.7,368 npm138MIT
- FlicenseAqualityDmaintenanceA TypeScript MCP server template with Zod validation, dual transport (stdio/HTTP), and modular architecture for building MCP-compatible tools, resources, and prompts.11-
- -licenseNot gradedqualityNot gradedmaintenanceA production-ready TypeScript MCP server providing basic tools (add, echo, timestamp), resources (server info, greetings, data access), and prompt templates (analyze, code-review, summarize). Serves as a foundation for building custom MCP servers with extensible architecture.221 npm-
- -licenseNot gradedqualityDmaintenanceA TypeScript implementation of a Model Context Protocol server and client that enables interaction with language models (specifically Mistral running on Ollama).-
- AlicenseBqualityCmaintenanceA TypeScript-based MCP server that provides tools to interact with local Codex and Gemini CLIs via stdio transport. It enables users to execute prompts through the ask_codex and ask_gemini tools, supporting custom models and timeout configurations.13 npm6MIT
TDQS
Scored across 8 tools
Most tools have distinct purposes (code completion, chat, OCR, vision, audio, workflow). However, mistral_chat and mistral_vision both handle chat, which could cause confusion despite descriptions clarifying modality differences. This minor overlap prevents a perfect score.
All tool names follow a consistent pattern: lowercase snake_case with a product/domain prefix (codestral_, mistral_, voxtral_, workflow_) followed by an action (fim, chat, ocr, vision, transcribe, execute, interact, status). This is highly predictable and uniform.
With 8 tools, the server covers multiple AI capabilities (code, chat, vision, OCR, audio) and workflow management without being bloated. Each tool earns its place, and the count aligns well with the server's stated purpose.
The tool surface is comprehensive for the advertised features: generation, vision, OCR, audio, and workflow lifecycle. Minor gaps exist (e.g., no model listing or file management), but they don't hinder core workflows. The set feels complete for its scope.