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Mcp Server

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phuryn
mcp-server

Before adding a remote MCP server or tool to a web product, adding OAuth to it, or listing it in a directory: ship it right the first time, tests included.

Overview

Publisherphuryn
Repositoryproduct-engineering
Skill namemcp-server
Stars
189
Forks
27
Bundled files
5
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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Mcp Server 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/phuryn/product-engineering/tree/main/app-stack/skills/mcp-server
  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/phuryn/product-engineering.git /tmp/product-engineering
mkdir -p .claude/skills
cp -r /tmp/product-engineering/app-stack/skills/mcp-server .claude/skills/mcp-server
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mcp Server 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 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 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

How to put a remote MCP server in front of outside agents and get it listed, written so the next product does not relearn it. The lessons come from products built on the same stack (Next.js + Supabase + Clerk + Netlify): a live Q&A product with an authenticated server, a stdio package and an anonymous ChatGPT plugin, and a monitoring product that decided against any unauthenticated surface. They appear here only as examples.

What is true of one product alone lives in that product's repository, in records this skill names by what they hold:

  • the API contract: every REST route and MCP tool, what it accepts, returns and refuses;
  • the distribution record: one row per listing (directory, registry, package), with its state, its date and how it is updated, and each client verified, with the date and what was called;
  • the decision log: dated decisions that had real options, each with its reopen trigger;
  • the backlog: deferred work, each item with the trigger that reopens it.

Clerk's settings for OAuth are not here. They are in clerk-production § MCP OAuth, with the four behaviours the route must allow for: a discovery list that is not the token's shape, a 503 for a verifier outage, an unknown kid refused before Clerk sees it, and revocation unseen until expiry. Read that section before writing the route; this skill does not repeat it.

What binds, in order

  1. saas-authorization. A tool is a route: its caller is resolved by the three functions, deterministic code decides what it may do, and everything it returns that a person wrote is untrusted text.
  2. clerk-production § MCP OAuth for every Clerk setting, applied and re-read per instance.
  3. This skill for the server, its tools, the directories, the clients and the package.
  4. The directories' own current pages. They change without notice: read them again before every submission, and record the date you read them. Where this skill and a directory's page disagree, the page wins and this skill gets a dated correction.

Decide these first, in the product's decision log

DecisionThe defaultThe other choice, and what it costsWhere
Does anything answer without sign-in?No: every call is authenticated, initialize included, by Clerk OAuth or a workspace API tokenAn anonymous surface buys users who have no account, such as a ChatGPT plugin that makes a room with no sign-up. It costs a new kind of caller, an identity only the platform can vouch for, limits and a lifetime of its own, a disclosure burden, and in OpenAI's directory a package that declares it before the first upload. A monitoring product declined it: "we do not do MCP without authentication"references/server.md § Who may call it · the chatgpt-plugin skill
Who may connect?An admitted OAuth caller gets the full grant a new API token carries, so admit by the permission that mints such a token, checked through the three functions on every callA role compared by name, or a neighbouring permission the same role happens to hold today, hands write to whichever role is given that permission later. A caller without the permission gets a clear 403, never a narrower connectionreferences/server.md § Who may call it
What must an agent never do?A written list, held in code, and said in the instructions and the READMEExamples: an audience product's agents cannot ask, vote or delete; a monitoring product's cannot delete, change billing, manage tokens or publish an incident notereferences/tools.md § Choosing the tools
Must a sign-up that starts in an agent work?Yes, for a product whose first user may arrive through an agentRefusing the first call until the dashboard has been opened sends that user out of the agent; a Clerk setting and a workspace made on the first authenticated call close the gapreferences/server.md § The first call
The path/api/mcp or /mcp, chosen onceThe OAuth audience and both protected-resource documents (the path's own and the root alias) name that exact URL, and the 401 challenge points at the path's own document; the authorization-server document stays Clerk's, relayedreferences/server.md § The route

The rules every change meets

  • Descriptions, titles and the server's instructions are product copy, and each sentence is a claim. Directories score them and show them; agents choose tools from them alone. Review them like code: one server's first independent round over rewritten descriptions found eight sentences the code did not keep, and they took five rounds to come back clean. references/tools.md.
  • Every argument has a description and every tool an outputSchema, and a test holds both against the real handlers. Smithery scores each out of 10, and a server without them scored 0 on both (October 2026). references/tools.md § The tests.
  • Every tool carries a title and the hints a directory requires. Claude's directory flags a tool without a title and without readOnlyHint or destructiveHint; OpenAI's listing check flagged a tool that closed something while declaring itself not destructive. references/tools.md § Annotations.
  • tools/list has a size budget, because every client loads it into the model's context. Pin it in a test: per-field output descriptions once took one server from 12 KB to 49 KB.
  • Log every call, refused ones included, before the server is listed anywhere. It is the only way to learn which clients and directories actually call you. references/server.md § The per-call log.
  • A client is supported only once a happy path with it is recorded, by this product, against this product, with the date, the client and what was called. A client another product verified, or someone reports working, is unverified for you. references/clients.md.
  • A change to the tool surface owes the package and the directories, after promotion, and a change to a description or a schema is a change to the surface: output schemas added to one hosted server never reached its package, which then offered a different surface from the one directories scored. references/companion-repo.md § After a product release.
  • Everything is published from the owner's account. The registry namespace comes from the GitHub login that publishes, and a listing's maintainer is whoever claimed it; check which account the CLI is logged in as before any listing (gh api user --jq .login).

The stage, in order

  1. REST functions first. MCP and REST call the same module, so the two can never disagree about what a call does. REST stays token-only; OAuth is for MCP clients.
  2. The route: stateless Streamable HTTP, the challenge, the three layers of error, the limits, the log (references/server.md).
  3. The tools and their two tests (references/tools.md).
  4. OAuth per clerk-production § MCP OAuth, then an automated real grant and a real client (references/server.md § Proving it).
  5. The docs: the API contract, a public page for developers pinned to the code by a test, and the distribution record.
  6. The release with its review rounds (the independent-review skill); descriptions and instructions are in the subject, not context.
  7. Listings (references/directories.md; a ChatGPT plugin, the chatgpt-plugin skill) and the package (references/companion-repo.md), after promotion, from what production serves.

How you can tell it worked

A real client connects to the deployed server, signs in, lists the tools and calls one as a member, and the tests cover each tool's permission check.

Files

FileHoldsRead when
references/server.mdwho may call it, and what an anonymous surface costs; the first call of an agent-first sign-up; the route, its errors and transport; limits; the per-call log; proving it worksbuilding or changing the route, its auth or its limits; deciding on an anonymous surface
references/tools.mdnaming; choosing the tools; descriptions, arguments, results, output schemas, annotations, the server's instructions; the two testsadding or changing any tool, or the instructions
references/directories.mdeach directory and registry: what it lists, requires, scores or refuses, and how a listing is kept currentlisting the server, answering a review, rescanning after a change
references/clients.mdeach client: how it connects, what to verify, what is known about itwriting setup instructions, or saying which clients work
references/companion-repo.mdthe public repository and the stdio package: layout, README, the package's rules, one version everywhere, the release procedure, what a product release owes itcreating the repository, changing the package, releasing it

When something is learned

  • This skill is general and will be published. Write the rule and why it holds. A product appears only as an example, named by kind ("a live Q&A product"), never by its commits, release codes, file paths, listing or plugin ids, pull-request numbers or account names.
  • Date what a platform or directory did or required ("read on", "observed in"), because they change without notice. When one changes, correct the line; do not leave the old one beside the new.
  • What is true of one product goes in that product's repository: its listings and their states, versions, verified clients, open reviews and receipts, in its distribution record, API contract or backlog.
  • A rule a test can hold gets the test, and the skill says what the test asserts, not where one product keeps it.

Where the copies live

  • Each product carries its own copy at .claude/skills/mcp-server/ (or .agents/skills/), and a lesson a product learns goes back to your master copy, generalized. diff -r <master>/mcp-server <product>/.claude/skills/mcp-server shows what has not.
  • A builder that is not Claude Code reads these as plain files. A stage brief for the MCP server names this folder by path.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Mcp Server AI skill do?

Before adding a remote MCP server or tool to a web product, adding OAuth to it, or listing it in a directory: ship it right the first time, tests included.

Why use Mcp Server on TypingMind?

Because you install it once and use it with any model. Mcp Server 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 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/phuryn/product-engineering/tree/main/app-stack/skills/mcp-server. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Mcp Server?

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?

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

Is the Mcp Server AI skill free?

Yes. It is published on GitHub by phuryn 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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