Agent Skills
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mcp-server-spec
GitHub专为AI代理设计MCP服务器的技能。将产品抽象为少量任务型工具，优化描述、返回值与错误处理，明确认证授权与安全边界，生成完整规范以提升代理可用性。
触发场景
需要设计MCP服务器
将产品暴露给AI代理
审查现有MCP服务器性能问题
为Claude或其他MCP客户端设计工具
安装
npx skills add mohitagw15856/pm-claude-skills --skill mcp-server-spec -g -y
SKILL.md
Frontmatter
{
"name": "mcp-server-spec",
"description": "Design an MCP server for a product — the tool surface, auth model, and safety boundaries that make it genuinely usable by AI agents. Use when asked to spec an MCP server, expose a product to agents, design tools for Claude or other MCP clients, or review why an existing MCP server performs badly. Produces a complete server spec: a small task-shaped toolset with agent-tested descriptions, auth and scoping decisions, error design, and an explicit not-exposed list."
}
MCP Server Spec Skill
Every SaaS is shipping an MCP server; most dump their REST API as forty tools and wonder why agents flail. This skill designs the server as what it actually is: a user interface for a non-human user — few tools, task-shaped, with descriptions written for a model deciding under uncertainty.
What This Skill Produces
- A toolset design: 3-10 tools mapped to agent tasks, not API endpoints
- Per-tool specs: name, description (the routing surface), parameters, returns, error behaviour
- Auth & scoping decisions: how credentials flow, what a token can never do
- An explicit not-exposed list with reasons — the most load-bearing section
- A test plan: the agent-eval loop that proves the toolset works
Required Inputs
Ask for (if not already provided):
- The product and what users hire it for (the top 5 jobs, not the feature list)
- The existing API surface (endpoints or capability list) if one exists
- Who the agent acts for — the end user's own account? a service account? multi-tenant?
- The riskiest actions the product supports (deletes, sends, payments, permission changes)
Design Method
- Start from agent tasks, not endpoints. List the 5-8 things an agent will actually be asked to do with this product ("file an expense", "find last quarter's report", "summarise ticket history"). Each becomes one tool — even if it spans four API calls internally. An endpoint-mirrored toolset makes the agent do your orchestration; a task-shaped one does it for them.
- Keep the toolset small. Every tool dilutes selection accuracy on every call. Target ≤10; past ~15, split into separately-loadable servers by workflow. Merge list/get/search variants behind one tool with parameters where natural.
- Write descriptions as routing surfaces. The description is all the model sees when choosing. Formula per tool: what it does (one clause) · when to use it and when to use the sibling tool instead · what it returns. Test: could a model pick correctly between your two closest tools from descriptions alone?
- Design returns for context windows. Return the 6 fields an agent needs, not the 60 the API has; include stable IDs for chaining; paginate with explicit
has_more; keep any response under ~2k tokens by default with an opt-in for detail. - Make errors instructive. An agent retries what it understands:
"date must be YYYY-MM-DD"beats400 Bad Request. Every error names the parameter at fault and the fix. - Draw the safety boundary. Classify every capability: expose (read/create, low blast radius) · expose gated (destructive/outward-facing — require an explicit confirmation parameter and document that clients should surface approval) · never expose (auth changes, deletes without recovery, bulk exports of other users' data). The never-list ships in the spec with reasons.
- Specify auth honestly. OAuth per end user (agent acts as the user, inherits their permissions) vs API key (service account — then per-tool scoping matters more). State token lifetime, revocation, and what happens mid-session on expiry.
Output Format
MCP Server Spec: [product]
Agent jobs served: [the 5-8 tasks] · Tool count: [n] · Auth: [model + scoping]
Tools
| Tool | Description (as shipped) | Key params | Returns | Risk class |
|---|
Gated actions: [which tools require confirmation params, and the expected client behaviour]
Never exposed: [capability → reason] (one line each; this list is reviewed like an API contract)
Error design: [the error shape + 3 example messages]
Test plan: [10-15 realistic agent prompts spanning the jobs; run against a real client; a tool whose description gets misselected twice gets rewritten, not documented around]
Quality Checks
- Every tool maps to an agent task; no tool exists because "the endpoint was there"
- Any two sibling tools are distinguishable from their descriptions alone
- Default responses fit comfortably in a context window (≤~2k tokens)
- Every destructive or outward-facing action is gated or on the never-list
- Errors name the offending parameter and the fix
- The spec includes the agent-eval test plan, not just the schema
Anti-Patterns
- Do not mirror the REST API — 40 endpoint-tools is the #1 way MCP servers fail
- Do not write descriptions for developers ("wraps the /v2/items endpoint") — write them for a model choosing a tool
- Do not return full API payloads — context windows are the scarce resource
- Do not expose destructive actions ungated because "the client will be careful"
- Do not skip the never-exposed list — an MCP server without one hasn't been threat-modelled
- Do not ship without running the agent test plan — schema-valid and agent-usable are different properties
版本历史
- a38bc30 当前 2026-07-05 11:38
