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

MCP server enabling AI agents to perform cross-chain token swaps through NEAR's intent-based architecture using the Defuse Protocol, with token discovery, quote retrieval, swap execution, and status tracking.

An MCP server that integrates AI agents with Figma, enabling reading and programmatic modification of designs.

Enables AI agents to directly control Figma Desktop via MCP, supporting UI creation, editing, prototyping, and variable management with over 60 tools.

Track how your brand appears in AI-generated answers across ChatGPT, Perplexity, and other AI models. Analyze visibility, sentiment, citations, and domain rankings with 31 tools — including analytics reports, chat inspection, query analysis, and full CRUD for brands, prompts, tags, and topics.

A simple MCP server that provides a basic greeting tool and serves as a starter template for AWS Lambda deployment. Demonstrates how to build and deploy MCP servers with both local development and cloud deployment capabilities.

Enables AI assistants to search multiple tech job boards at once, summarize what the market is asking for, and rank postings by how well they match a user's own skills.

AI job search and job application tracker. Search live jobs by title, location, salary, date posted, and remote or on-site, and see each one checked on real data: pay against market rates, ghost-job signals, visa sponsorship history, and how well it matches your resume. Check how hiring software reads your resume, track applications, set follow-up reminders, and get email alerts.

An intelligence layer for discovering and orchestrating Japanese SaaS MCP tools through intent-based search and workflow recipes. It enables AI agents to evaluate service quality using community-driven insights while ensuring privacy through automated PII masking.

Enables coding agents to compose, tune, render, mix, and audit native VOCALOID3/4 projects from scratch, acting as a production bridge between intent and finished song.

Enables AI assistants to read and modify Figma designs programmatically through the Model Context Protocol.

A lightweight MCP server that enables AI assistants to execute local development tools and retrieve system status with low latency over stdio or HTTP.

An MCP server that provides AI clients with access to developer documentation via llms.txt files. Exposes tools, resources, and prompts.

A local-first, no-API-key MCP server that enables LLMs to search the web, fetch pages, and read documents using multiple engines and smart fallbacks.

Enables AI assistants to perform Google searches with real, resolvable sources and transparent search terms.

MCP server for overseer.nvim — gives any MCP-capable coding agent running inside Neovim (Claude Code, Codex, Gemini CLI, opencode) the same visibility and control over tasks the user has: list, run, tail, restart, stop. Agent-started dev servers land in the user's own task list instead of becoming invisible orphaned processes.

Deterministic encode, decode, hash and convert for agents, on the same pipeline engine as the String Utility Belt web app, CLI and editor extensions: a pipeline built and checked in the browser runs unchanged from its share link. Base64/hex/URL, JWT, hashes, JSON/YAML/CSV/XML, dates and regex; tools to search, describe and run utilities or whole pipelines, and to detect input formats. Runs locally

Enables AI models to access and search offline Wikipedia and other knowledge bases stored in ZIM format files. Provides intelligent content retrieval, structured browsing, advanced search capabilities, and metadata extraction for comprehensive offline knowledge access.

Enables LLMs to interact with the AT Protocol ecosystem, including Bluesky, through natural language. Supports public data access without authentication and full write operations with authentication.

Enables AI assistants to read, search, and traverse your entire reMarkable library, including handwritten notes via OCR.

A TypeScript-based server that enables programmatic management of pseudo-terminal sessions. It allows users to spawn, control, and interact with terminal processes in real-time through the Model Context Protocol.