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Add contributing guidelines, problem statement updates, and related work documentation

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olaservo 2026-02-03 09:43:37 -07:00
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# Problem Statement
Native "skills" support in host applications demonstrates demand for rich workflow instructions, but there's no convention for exposing equivalent functionality through MCP primitives.
[Agent Skills](https://agentskills.io/home) support in AI applications demonstrates demand for rich workflow instructions, but there's no convention for exposing equivalent functionality through MCP primitives.
## Current Limitations
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## Key Use Cases
### 1. Complex Workflow Orchestration
See [use-cases.md](use-cases.md) for detailed use cases and community input. In summary:
Bob Dickinson's [mcpGraph](https://github.com/TeamSparkAI/mcpGraph) toolkit requires a skill file of 875+ lines to instruct agents on building directed graphs of MCP nodes. This orchestration logic is "way more than you'd want to put in instructions."
### 2. Conditional Workflows
Workflows that reference tools conditionally based on context, requiring rich structured instructions that can be dynamically loaded.
### 3. Multi-Server Composition
Skills that leverage tools from multiple off-the-shelf servers where you can't (or don't want to) modify their individual instructions.
### 4. Progressive Disclosure
Skills broken into linked sets of files for effective context management, loaded progressively as the agent needs them rather than all at once.
1. **Complex Workflow Orchestration** — Multi-step workflows requiring 875+ lines of instruction (e.g., [mcpGraph](https://github.com/TeamSparkAI/mcpGraph))
2. **Conditional Workflows** — Branching instructions dynamically loaded based on context
3. **Multi-Server Composition** — Skills leveraging tools from multiple off-the-shelf servers
4. **Progressive Disclosure** — Skills broken into linked file sets, loaded on demand
## Open Questions
1. **Is this a registry problem or an MCP server problem?** Should skills be discoverable through registry metadata ("if you install this server, also install this skill") or contained within the MCP server itself?
2. **How do "first-class" skills differ from "skills as context"?** Native agent skills can be presented through the user agent, bundled with subagents, etc. Do MCP-surfaced skills lose capabilities compared to directly installed skills?
3. **Should server.instructions be extended for richer content?** Or is the separation between "primitive server" and "skill that uses the primitive" the right abstraction?
4. **How should skills relate to multiple servers?** A skill orchestrating tools from several servers can't live in any single server's instructions.
5. **Do clients actually leverage skills when presented via MCP?** Early experiments suggest they do, but more rigorous testing is needed.
6. **How do we coordinate with agent skills spec owners?** The contribution model for the skills spec isn't clear, and MCP-related efforts should be brought to their attention.
7. **What would MCP have had to get right for skills to have been shipped over MCP from the beginning?** (https://github.com/keithagroves)
8. **What could MCP reasonably change so that it will be the obvious choice for new formats?** (https://github.com/keithagroves)
9. **Who gets visibility into skill content, and who decides when it gets loaded?** The control model question — model-controlled vs. application-controlled. (https://github.com/olaservo)
10. **How should skills handle security and trust boundaries?** If skills can be abused for prompt injection, what mitigations should be spec'd? (provenance, gating, explicit policy) (https://github.com/Agent-Hellboy)
11. **Should the control model be use-case specific?** Perhaps resources (application-controlled) for some use cases, tools (model-controlled) for others? Can a convention support both? (https://github.com/olaservo)
See [open-questions.md](open-questions.md) for the full list of unresolved questions with community input.