How does this skill prevent context window pollution?

It uses a 'progressive disclosure' approach where it lists tools in an external JSON file and only injects the specific tool definitions into the main context when they are relevant to the current task.

What is the Model Context Protocol (MCP)?

MCP is an open standard that allows AI agents to securely connect with external tools, data sources, and resources, enabling them to perform actions outside their native environment.

Can I connect to multiple MCP servers at once?

Yes, this skill supports multi-server management through a unified configuration file, aggregating capabilities from all connected servers into a single interface.

Does this skill work with the Gemini CLI?

Yes, it is designed to integrate with Gemini CLI as the primary execution engine for automated tool discovery and natural language command processing.

MCP Management

Name: MCP Management
Author: nordeim

bynordeim

Developer Tools

Orchestrates Model Context Protocol (MCP) servers to discover, analyze, and execute external tools while maintaining context window efficiency.

About

The MCP Management skill provides a standardized framework for interacting with Model Context Protocol (MCP) servers, allowing Claude to connect with external tools and data sources. It solves the problem of context window bloat by utilizing progressive disclosure—only loading specific tool definitions when they are required for a task. With support for multi-server management, intelligent LLM-driven tool selection, and seamless Gemini CLI integration, this skill enables developers to build highly capable agentic workflows that can programmatically interact with their local or remote environments.

Key Features

Progressive disclosure of MCP capabilities to maximize context window efficiency
Seamless integration with Gemini CLI for natural language tool execution
Multi-server orchestration from a centralized .mcp.json configuration
Intelligent tool selection using LLM analysis of local tool catalogs
0 GitHub stars
Automated discovery and JSON persistence of tools, prompts, and resources

Use Cases

Managing complex AI agent workflows that require tools from multiple different MCP servers
Building and debugging custom MCP client implementations with standardized scripts
Reducing context overhead by delegating tool discovery and execution to subagents

About

Key Features

Progressive disclosure of MCP capabilities to maximize context window efficiency
Seamless integration with Gemini CLI for natural language tool execution
Multi-server orchestration from a centralized .mcp.json configuration
Intelligent tool selection using LLM analysis of local tool catalogs
0 GitHub stars
Automated discovery and JSON persistence of tools, prompts, and resources

Use Cases

Managing complex AI agent workflows that require tools from multiple different MCP servers
Building and debugging custom MCP client implementations with standardized scripts
Reducing context overhead by delegating tool discovery and execution to subagents