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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.
MCP's value for skills isn't just distribution, it's the interaction model. MCP defines app, human, and assistant roles, giving skills a built-in framework for control model decisions (who sees the content, who decides when it loads). This makes MCP a natural complement to skills as a delivery channel for workflow instructions.
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.