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Mcp Server Spec

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mohitagw15856
mcp-server-spec

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.

Overview

Publishermohitagw15856
Repositorypm-claude-skills
Skill namemcp-server-spec
Stars
1.4K
Forks
257
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by mohitagw15856 on GitHub. Read the source before you install it.

Installation

Install the Mcp Server Spec AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/mohitagw15856/pm-claude-skills/tree/main/plugins/pm-agentnative/skills/mcp-server-spec
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git /tmp/pm-claude-skills
mkdir -p .claude/skills
cp -r /tmp/pm-claude-skills/plugins/pm-agentnative/skills/mcp-server-spec .claude/skills/mcp-server-spec
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mcp Server Spec in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Mcp Server Spec on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Mcp Server Spec is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

  1. 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.
  2. 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.
  3. 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?
  4. 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.
  5. Make errors instructive. An agent retries what it understands: "date must be YYYY-MM-DD" beats 400 Bad Request. Every error names the parameter at fault and the fix.
  6. 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.
  7. 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

ToolDescription (as shipped)Key paramsReturnsRisk 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

Example Trigger Phrases

  • "Spec an MCP server."
  • "Expose a product to agents."
  • "Design tools for Claude."
  • "Review why an existing MCP server performs badly."

Frequently asked questions

What does the Mcp Server Spec AI skill do?

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.

Why use Mcp Server Spec on TypingMind?

Because you install it once and use it with any model. Mcp Server Spec is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Mcp Server Spec in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mohitagw15856/pm-claude-skills/tree/main/plugins/pm-agentnative/skills/mcp-server-spec. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Mcp Server Spec?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Mcp Server Spec?

As many as you like. As long as a model supports skills, you can use Mcp Server Spec with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mcp Server Spec AI skill free?

Yes. It is published on GitHub by mohitagw15856 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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