"How to configure Azure Speech" matching MCP servers:

Enables large language models to automatically report customer-facing issues with Azure resources by parsing natural language requests and submitting impact reports through the Azure Management API.

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 Codex to delegate coding tasks to an OpenCode CLI locally, returning structured results such as exit codes, session summaries, tool calls, and git diffs.

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.

Enables routing tasks to the right specialist agents, assembling the needed context from a layered catalog of agents, skills, knowledge, runbooks, topics, tools, and connectors, while remembering how a business wants work done.

Teach any AI agent how you use an MCP server – Soft-Landing to your AI integrations

This MCP server enables remote control and management of Claude Code agents, allowing you to execute missions, configure agent personalities, and integrate with other MCP tools.

Enables learning how to build and use an MCP server with tools, resources, and prompts through a Python example.

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 MCP clients to submit completed speech or typed text to a self-hosted decision engine and receive judgments such as ordinary speech, command, retained thought, uncertainty, or no action, along with session and decision inspection.

Enables the creation and management of BotUyo AI agents directly from MCP-compatible tools. Users can configure agent identities, conversation stages, and tools through natural language without manual cloning or building.

An educational implementation of a Model Context Protocol server that demonstrates how to build a functional MCP server for integrating with various LLM clients like Claude Desktop.

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.

An MCP server that lets an AI agent drive Unreal Engine 5: create projects, import assets, build levels and Blueprints, configure replication, compile C++, run Play In Editor and package the game.

Enables AI assistants to control Anki Vector robots locally via natural language, providing tools for speech, motion, perception, and interaction without cloud dependency.

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

An educational implementation of a Model Context Protocol server that demonstrates how to build a functional MCP server integrating with various LLM clients.

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.