"Flask" matching MCP servers:

Flask Extension for apcore (AI-Perceivable Core) integration. Expose your Flask routes as MCP tools with auto-discovery, Pydantic schema inference, and built-in observability.

Flask-based Model Context Protocol (MCP) server for Python. Drop it into any Flask app or run it standalone.

Flask-MCP-Plus enables developers to create MCP servers using Flask, with support for defining tools, resources, and prompts with automatic schema generation and type safety.

Video feedback for AI agents via Flask (flask.do). Agents upload renders for human review and get the feedback back as transcribed, timestamped comments over MCP, with annotated frames and versioned revisions.

Automatically converts Python web backends (FastAPI, Flask, Django) into MCP servers by exposing API routes as MCP tools with near-zero boilerplate.

A lightweight Flask-based server that enables Claude AI to perform real-time web searches via DuckDuckGo during conversations, allowing the AI to access current information.

Launches HexStrike AI with an ngrok HTTPS tunnel, starting a Flask API and MCP bridge for remote interaction.

A Flask-based chatbot using Gemini 2.5 Flash and MCP tools to manage GitHub repositories and LinkedIn profiles via natural language.

A Flask-based server that hosts multiple tools, each exposing functionalities by calling external REST APIs through a unified interface.

A Model Context Protocol server that wraps the free GLM-4.6V-Flash vision model, enabling text-only LLM clients like Codex, Cursor, and Claude Desktop to analyze images, videos, and files (PDF/TXT) through standard MCP tools.

Live sports betting player props MCP server covering NBA, MLB, NFL, NHL, NCAA, and soccer. Unified from real sportsbooks into one REST API and a real MCP server (Streamable HTTP). Free tier, no card required.

Enables text-to-image generation, image editing, and multi-image composition using Google's Gemini 2.5 Flash Image API. Supports flexible aspect ratios and character consistency across generations.

Enables MCP-compatible IDEs to generate images and videos using Agnes 2.5 flash models through natural language prompts, with tools for image generation, asynchronous video creation, and video polling.

Enables AI coding agents to make fast, zero-output-token decisions by evaluating context, diffs, logs, or options through the OpenRouter Decisions API using TypeSafe Jev, returning calibrated probabilities for binary, categorical, or scoring questions.

Exposes video generation and editing tools to MCP-capable clients, enabling text-to-video, image-to-video, video editing, reusable voice and character profiles, and task status queries through natural language.

Give your AI coding assistant live visibility into the user's browser. Claude sees the actual DOM, console errors, and network timing instead of guessing from screenshots. Drop-in middleware for FastAPI + Flask.

Flask-based server that exposes callable tools via HTTP endpoints for AI agents like Gemini CLI, enabling agent orchestration, tool introspection, and workflow automation with a centralized tool registry.

A Flask-based server that implements Model Context Protocol to enhance LLMs with external tool capabilities via natural language, allowing tools like weather lookup and calculations to be invoked directly in the model's text output.

A Flask-based MCP server designed for testing deployment on Google App Engine. Provides a deeplink checking endpoint that accepts flattened JSON parameters and forwards them as nested objects to external APIs.