SkillMesh: MCP-Friendly Router for Large Tool Catalogs Reduces Context Size by 70%
SkillMesh is an MCP-friendly router designed to handle large tool and skill catalogs for AI agents. The creator identified that loading entire tool catalogs into every prompt hurts tool selection accuracy and inflates token costs as catalogs grow.
How SkillMesh Works
The approach focuses on selective context injection:
- Retrieves top-K relevant expert cards for the current query
- Injects only those cards into the agent's context
- Keeps the rest of the catalog out of the prompt
This reduces context size by approximately 70% in many cases and enables agents to scale across multiple domains without prompt bloat.
Current Features
SkillMesh currently supports:
- Claude integration via MCP server (
skillmesh-mcp) - Codex skill bundle integration
- OpenAI-style function schema in tool invocation metadata
Tools and capabilities can be installed by role, adding relevant functionality based on the task.
Example Use Case
For a query like "clean sales data, train a baseline model, and generate charts," SkillMesh would route to only relevant data processing, machine learning, and visualization expert cards instead of loading the entire catalog.
Installation and Feedback
The project is available on GitHub at SkillMesh. The creator is seeking feedback on:
- Retrieval quality (whether it picks the right tools)
- Registry format (ease of adding new tools)
- MCP integration ergonomics
📖 Read the full source: r/LocalLLaMA
👀 See Also
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