mcp-code-execution

What problem does it solve?

This Skill addresses the challenge of context pollution and high token usage when AI agents interact with Model Context Protocol (MCP) servers, especially those with large tool sets. It enables efficient, on-demand tool loading and data filtering, drastically reducing token costs and improving response times.

Core Features & Use Cases

• Progressive Tool Discovery: Load tool definitions only when needed, avoiding upfront context bloat.
• Context-Efficient Data Processing: Process large datasets in the execution environment and return only filtered summaries to the AI's context.
• Multi-Agent Workflow Support: Facilitate complex tasks by delegating to specialized sub-agents (Discovery, Execution, Filtering) with minimal context overlap.
• Use Case: When integrating Claude with a new external service (e.g., GitHub, Salesforce) that has many tools, use this skill to set up a context-efficient integration that only loads the necessary tools and data, saving thousands of tokens per interaction.

Quick Start

Explain how to add a new MCP server named 'github' to the registry and create its corresponding helper script for context-efficient operations.