mess-mcp
An MCP server enabling Claude to dispatch and manage physical-world tasks using the MESS (Meatspace Execution and Submission System) protocol.
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108 skills found
An MCP server enabling Claude to dispatch and manage physical-world tasks using the MESS (Meatspace Execution and Submission System) protocol.
Test C# Model Context Protocol (MCP) servers using unit tests for tools and integration tests for protocol compliance and end-to-end scenarios.
Easily configure and add Model Context Protocol (MCP) servers to various AI coding clients like Cursor, Claude, VS Code, and more using an interactive or automated command-line interface.
A guide for building high-quality MCP (Model Context Protocol) servers in Python or TypeScript to integrate external APIs and services into LLM workflows.
Semantic Go code navigation and analysis tool using the Language Server Protocol (LSP) for accurate, high-performance project intelligence.
Universal MCP client for connecting to any MCP server with progressive disclosure. Wraps MCP servers as skills to prevent context window bloat from tool definitions. Use for Zapier, GitHub, sequential thinking, and file operations.
Manage, deploy, and debug GitHub MCP servers and gateways. Orchestrate Docker-based MCP containers, troubleshoot connectivity, handle authentication, and integrate with Copilot CLI and Agentic Workflow Firewalls.
An MCP server enabling agents to edit, manage, and compile Arduino IDE 2.0 sketches, including source code manipulation and automated build capabilities via arduino-cli.
Repository implementation guide for local-skills-mcp. Provides technical documentation on MCP tool handlers, skill loading, aggregation logic, and project structure for developers.
Integrate Snowflake with MCP clients. Manage Snowflake endpoints, validate connectivity, and leverage Cortex AI (Search, Analyst, Agent) services directly within your AI workflow.
Operate Google Tag Manager via MCP. Handles OAuth, resource discovery, and CRUD operations for tags, triggers, and variables directly from your LLM agent.
Process massive files and large codebases (10M+ tokens) by recursively chunking, sub-querying, and aggregating results to overcome LLM context limits.