ext-skills/docs/approaches.md

# Approaches Being Explored

> These are not mutually exclusive solutions.

## Design Principles

Whatever approach is adopted, community members have highlighted several principles:

> "There should be intentional focus on making it easy for server authors to create and expose skills... client hosts are strongly incentivized to have a relatively uniform way to discover and consume them — at least from the point of view of a server author — while also leaving room for client host innovation." — [Peder Holdgaard Pedersen](https://github.com/PederHP)

## Central Tension: Convention vs. Protocol Extension

The approaches below span a spectrum. At one end, skills become a first-class MCP primitive with dedicated protocol methods (Approach 1). At the other, existing primitives are used with documented conventions (Approach 6). A key question for this IG is whether convention can prove patterns before standardization — or whether the ecosystem needs protocol-level support to achieve reliable interoperability. These are not mutually exclusive; convention work can inform and de-risk a future protocol extension.

## 1. Skills as MCP Primitives

Add Agent Skills as a first-class, discoverable primitive in MCP. A skill is a named bundle of instructions plus references to tools, prompts, and resources that together teach an agent how to perform a domain-specific workflow.

**Proposal:** [SEP-2076](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/2076)

**Author:** [Yu Yi](https://github.com/erain)

**Key elements:**

- New protocol methods: `skills/list` and `skills/get`
- A `skills` server capability
- A `notifications/skills/list_changed` notification
- Progressive disclosure: clients load skill summaries at startup, fetch full instructions on demand
- Mapping to existing SKILL.md format

**Status:** Draft, seeking sponsor.

**Community input:**

> "My main motivation is: we have so many MCP servers already available, how can we leverage them to distribute Skills?" — [Yu Yi](https://github.com/erain)

## 2. Skills as Registry Metadata

Add skill references to MCP registry entries so users know to install associated skills alongside servers.

**Implementations:** 
- [Ozz](https://github.com/JAORMX) started a discussion around [skills.json format proposal](https://github.com/modelcontextprotocol/registry/discussions/895)
- NimbleBrain has implemented this via a `skill` field in registry `_meta`, supporting `.skill` artifact bundles (ZIP containing SKILL.md + manifest.json).
    - https://github.com/NimbleBrainInc/skills
    - [registry.nimbletools.ai](https://registry.nimbletools.ai/)

**Community input:**

> "We view skills as an opportunity to use them as both a standalone (general-purpose capabilities) & MCP-paired (tool-specific guidance). For the main registry, the binding could be softer: optional fields like suggestedSkills or recommendedSkills rather than definitive pairing, since skill authorship is often decoupled from server authorship." — [Mat Goldsborough](https://github.com/mgoldsborough)

## 3. Skills as Tools

Expose skills via tools like `list_skills` and `read_skills`. Server instructions can direct the agent to call the skill tool first.

**Implementations:** 

- [skilljack-mcp](https://github.com/olaservo/skilljack-mcp)
- [skills-over-mcp](https://github.com/keithagroves/skills-over-mcp)

**Community input:**

> "I wonder if a better way to approach this with existing primitives is by implementing a Skill() tool and establishing this as a standard recommendation for servers and clients, rather than adding a new primitive to MCP." — [Ola Hungerford](https://github.com/olaservo)

This approach may also:

- Expose skills as MCP resources
- Use Prompts for explicit skill invocation
- Use `tools/listChanged` and other notifications for dynamic updates without server re-initialization

### Variant: Skills via Sampling

Instead of exposing skill tools to the main agent, use MCP's Sampling with Tools capability ([SEP-1577](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/1577)). The server requests a sampling call with skill-specific tools (`read_skill_md`, `execute_script`, etc.) that are only visible during that sampling request. This keeps skill tools hidden from the main agent, addressing tool bloat. The server orchestrates skill execution; the main agent just sees the result.

**Caveat:** Sampling has limited client support currently.

**Source:** [jbnitorum](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/2076#issuecomment-3806151745)

## 4. Gateway/Composition Pattern

A gateway-style server that provides a group of MCPs as one, ensuring they get requisite configuration and instructions to work in harmony.

This pattern could enable loading primitives (tools) without the full "server" boundary.

**Community input:**

> "MCP repos can also be used as libraries... a gateway in same language as bundle servers could also load primitives like tools, and not necessarily need or want the 'server' parts." [SamMorrowDrums](https://github.com/SamMorrowDrums)

## 5. Server Instructions Reference

Use server instructions as a pointer to a resource: "If you need to do X, fetch resource Y for further instructions." This defers loading skill content until needed, managing context more efficiently.

**Limitation:** May not work with off-the-shelf servers where you can't modify their instructions.

## 6. Official Convention as Intermediate Step

A documented "MCP Skills Convention" as a middle path between ad-hoc experiments and protocol extension. This could:

- Define well-known URI schemes or naming patterns (e.g., resources matching `**/SKILL.md`)
- Recommend metadata structure (version, tags, dependencies) aligned with agentskills.io
- Provide guidance on control model: resources for application-controlled, `skill()` tool for model-controlled
- Be documented in MCP docs as a "Pattern" — not in protocol schema, but officially recommended
- Allow data gathering on adoption before considering protocol-level changes

This mirrors how other ecosystems (e.g., Kubernetes) graduate patterns: start as convention, prove value, then formalize. Could be a concrete IG deliverable: "MCP Skills Convention v0.1."

**Advantages of the convention approach:**

- Since MCP supports dynamically updating tools, the latest skills manifest can be included in tool descriptions
- Skills can also be modeled as Resources (using `skill://` URI) for application-controlled access
- Prompts could support explicit skill invocation
- The convention and protocol extension approaches are not mutually exclusive — convention can prove patterns before standardization