"How to implement intent recognition in MCP" matching MCP servers:

Provides image recognition capabilities using Anthropic Claude Vision and OpenAI GPT-4 Vision APIs, supporting multiple image formats and offering optional text extraction via Tesseract OCR.

Enables agents to convert Markdown into WhatsApp-compatible formatting, including styled text, lists, and tables that fit a phone's monospace width.

Turn natural language into 3D models in Fusion 360. 64 CAD tools including sketches, extrudes, fillets,and JIS standard parts.

Give any LLM agent a real Android or iPhone. 62 MCP tools: tap, swipe, type, screenshot, screen-tree reading, app launch, camera, TTS, crash reports, batched execution. Android via ADB, iPhone via WebDriverAgent, on-device inference, Docker+KVM emulators. Works with Claude Code, Cursor, LangChain, LlamaIndex, and any MCP client. MIT.

Enables agents to translate natural-language shopping intent into aggregated multi-merchant item discovery, consolidated cross-merchant carts, and structured transaction checkout payloads. Runs as a zero-dependency, MCP-compatible engine that can also be imported directly as a Python module.

Enables agents to translate consumer shopping intent into aggregated item discovery, cross-merchant cart consolidation, and structured transaction checkout payloads across multiple merchants. It runs as a zero-dependency Python MCP server or importable module compatible with Claude Desktop, Cursor, and other work agent frameworks.

Enables forwarding Douyin or Bilibili video shares to Kimi for AI analysis and automatically writing the resulting Chinese notes into an Obsidian vault.

A standalone proxy that transforms any OpenAPI or Swagger-described REST API into an MCP server by mapping API operations to executable MCP tools. It enables AI clients to interact with existing web services through automated HTTP requests based on their official documentation.

Turns any OpenAPI/Swagger or REST API into callable MCP tools for Claude, ChatGPT, Copilot, and Cursor, with secure authentication and role-based access control.

Automatically converts Swagger/OpenAPI specifications into dynamic MCP tools, enabling interaction with any REST API through natural language by loading specs from local files or URLs.

Minimal MCP server for sending messages to Poke via the inbound webhook.

Enables AI assistants to interact with humans through GUI dialogs for text input, choices, confirmations, and information display.

Converts REST API code (like NestJS controllers or FastAPI endpoints) to Postman collections and environments, helping developers automatically sync their API endpoints with Postman.

Enables MCP clients to inspect FreeCAD documents and export named solids to STL, find and derive OrcaSlicer presets, preview and run slices with recorded manifests, and then read G-code metadata, query a Moonraker printer, upload G-code, and start an uploaded print as a separate confirmed step.

Enables users to control the cursor in Figma through verbal commands using an agentic AI agent, streamlining the design process with a new interaction method.

Enables Cursor AI to interact with Figma designs, allowing users to read design information and programmatically modify elements through natural language commands.

An MCP server that converts Excel and Apple Numbers files to PDF format, enabling AI assistants like Claude to perform file conversion directly through conversation.

Enables Cursor AI to communicate with Figma for reading designs and modifying them programmatically, allowing users to automate design tasks through natural language.

Automates the conversion of Figma designs into TypeScript React components and integrates with GitHub to create pull requests for the generated code. It includes visual regression testing with Playwright and accessibility validation to ensure implementations match the original designs.

Allows Cursor AI to communicate with Figma for reading designs and modifying them programmatically through a Model Context Protocol integration.