"Steps to Internationalize and Localize My Application with Translations" matching MCP servers:

Provides an interactive checklist tool that allows AI agents to present step-by-step instructions to users through an automatically opened terminal UI. It enables agents to guide users through manual tasks and wait for completion, skipping, or feedback before proceeding.

Exposes tools that let an agent read a CV, search the Arbeitnow job board for Berlin AI/ML working student roles, score each vacancy against the CV, draft cover letters whose every claim cites CV facts, and queue them for human approval. The same tools can be driven from other MCP clients such as Claude Desktop, with scoring done deterministically in code and limits enforced outside the prompt.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Enables local Laya-MLX typed decisions (classification, scoring, risk routing, and yes/no) for coding agents like Codex, Claude Code, DeepSeek Harness, and Pi via MCP, running entirely on Apple Silicon Macs.

A Model Context Protocol server that converts Markdown content to HTML format.

A deterministic MCP server that executes 10-step topology pipelines on Cloudflare Workers, providing structured progress snapshots and adaptive JSON templates for AI agents.

Enables MCP clients to interact with A2A agents through four ordinary tools, translating agent discovery, messaging, task reading, and cancellation between the A2A and MCP protocols, with support for the tasks extension.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

An MCP server that turns a one-sentence idea into working, tested code using a pipeline of six AI agents, with optional deployment.

Enables MCP clients such as Claude Desktop to hand tasks to a running DeepSeek Harness window, where each task becomes a real session visible in the sidebar with a reusable session id. If the in-process plugin is unavailable, it falls back to running the task headless so the tool still returns a result.

A server that leverages the XMCP framework to discover and execute tools from the src/tools directory, supporting both SSE and STDIO transport methods for interaction.

MCP server for Android Emulators to have easier experience with LLMs

A MCP server that provides persistent memory for AI assistants, storing personal information, relationships, and observations to enable personalized and contextual conversations.

Enables users to turn raw video clips into finished, beat-synced montage cuts through natural-language chat with compatible coding agents, handling shot selection, music matching, subtitles, transitions, picture-in-picture, and rendering while allowing review before rendering.

Enables users to turn raw video clips into finished, beat-synced montage cuts through natural-language chat, automating shot selection, pacing, music matching, subtitles, transitions, picture-in-picture, and rendering. It returns reviewable timeline digests and contact sheets before rendering so edits can be approved in chat.

Enables coding agents to turn a folder of raw clips plus a plain-language prompt into a finished montage video, handling shot selection, pacing, beat-synced cuts, music matching, subtitles, transitions, and picture-in-picture. It returns a reviewable timeline digest and contact sheet before rendering, so nothing is exported until the user approves the plan.

This MCP server enables users to turn folders of raw video clips into finished, beat-synced montage cuts through natural-language chat, automating shot selection, music matching, subtitles, transitions, picture-in-picture, and rendering.

MCP server that lets you drive Claude Code sessions hands-free by voice from any MCP client, enabling remote models to send prompts, monitor progress, and get results read back.

An autonomous MCP server that uses the Claude Code CLI to solve coding problems without permission prompts.

One-pass agentic inbox triage as an MCP server: fetch unread Gmail → classify (action_needed/fyi/newsletter/noise) → summarize → extract tasks → draft replies as Gmail DRAFTS (never sends) → flag calendar → write a triage report. Four stdio tools (fetch_emails, save_gmail_draft, append_tasks, write_report); the host is the LLM, so it runs keyless in Claude Code. Gmail scopes: readonly + compose

Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.

An MCP server integration that enables Cursor AI to communicate with Figma, allowing users to read designs and modify them programmatically through natural language commands.

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 two LLMs to play Tic-Tac-Toe against each other autonomously using a shared tool and an SSE relay. The server facilitates agent-to-agent communication by holding tool responses until the opponent makes a move, managing the game state in real-time.

Enables Claude to use Google Gemini as a secondary AI through MCP for large-scale codebase analysis and complex reasoning tasks. Supports both Gemini Flash and Pro models with specialized functions for general queries and comprehensive code analysis.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

A Model Context Protocol server that exposes multiple AI tools over SSE transport with JWT-based secure authentication, allowing for dynamic tool registration and session management.

MCP server for qualifying and responding to inbound leads in seconds using a multi-agent AI pipeline.

MCP server for step-by-step mathematical reasoning and planning, enabling AI agents to execute calculations and perform GUI actions like opening PowerPoint.