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Problem Statement
Agent Skills support in AI applications demonstrates demand for rich workflow instructions, but there's no convention for exposing equivalent functionality through MCP primitives.
Current Limitations
- Server instructions load only at initialization — new or updated skills require re-initializing the server
- Complex workflows exceed practical instruction size — some skills require hundreds of lines of markdown with references to bundled files, scripts, and examples
- No discovery mechanism — users installing MCP servers from a registry don't know if there's a corresponding skill they should also install
- Multi-server orchestration — skills may need to coordinate tools from multiple servers, which doesn't fit the single-server instruction model
Broader Context
Skills discoverability is a specific instance of a more general MCP challenge: context-as-resources discoverability and standardization of client host behavior around non-tool primitives. The ecosystem has largely optimized for tools, and patterns for how clients discover and consume other forms of context — including skills — remain underdeveloped. Solutions in this space are likely to have implications beyond skills alone.
Key Use Cases
See use-cases.md for detailed use cases and community input. In summary:
- Complex Workflow Orchestration — Multi-step workflows requiring 875+ lines of instruction (e.g., mcpGraph)
- Conditional Workflows — Branching instructions dynamically loaded based on context
- Multi-Server Composition — Skills leveraging tools from multiple off-the-shelf servers
- Progressive Disclosure — Skills broken into linked file sets, loaded on demand
Open Questions
See open-questions.md for the full list of unresolved questions with community input.