MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
Build an MCP server end to end, tailored to how it will be used.
$ npx skills add techwolf-ai/ai-first-toolkit --skill build-mcp -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install techwolf-ai/ai-first-toolkit build-mcp --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/techwolf-ai/ai-first-toolkit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/tool-build-kit/skills/build-mcp .claude/skills/build-mcp && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "build-mcp" agent skill from https://github.com/techwolf-ai/ai-first-toolkit/tree/main/plugins/tool-build-kit/skills/build-mcp into .claude/skills/build-mcp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "build-mcp", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/techwolf-ai/ai-first-toolkit/tree/main/plugins/tool-build-kit/skills/build-mcpType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add techwolf-ai/ai-first-toolkit --skill build-mcp -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install techwolf-ai/ai-first-toolkit build-mcp --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/techwolf-ai/ai-first-toolkit.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/tool-build-kit/skills/build-mcp .agents/skills/build-mcp && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "build-mcp" agent skill from https://github.com/techwolf-ai/ai-first-toolkit/tree/main/plugins/tool-build-kit/skills/build-mcp into .agents/skills/build-mcp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "build-mcp", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add techwolf-ai/ai-first-toolkit --skill build-mcp -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install techwolf-ai/ai-first-toolkit build-mcp --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/techwolf-ai/ai-first-toolkit.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/tool-build-kit/skills/build-mcp .cursor/skills/build-mcp && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "build-mcp" agent skill from https://github.com/techwolf-ai/ai-first-toolkit/tree/main/plugins/tool-build-kit/skills/build-mcp into .cursor/skills/build-mcp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "build-mcp", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/techwolf-ai/ai-first-toolkit.git --path plugins/tool-build-kit/skills/build-mcp--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add techwolf-ai/ai-first-toolkit --skill build-mcp -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install techwolf-ai/ai-first-toolkit build-mcp --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/techwolf-ai/ai-first-toolkit.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/tool-build-kit/skills/build-mcp .gemini/skills/build-mcp && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "build-mcp" agent skill from https://github.com/techwolf-ai/ai-first-toolkit/tree/main/plugins/tool-build-kit/skills/build-mcp into .gemini/skills/build-mcp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "build-mcp", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install techwolf-ai/ai-first-toolkit build-mcpInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add techwolf-ai/ai-first-toolkit --skill build-mcp -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/techwolf-ai/ai-first-toolkit.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/tool-build-kit/skills/build-mcp .github/skills/build-mcp && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "build-mcp" agent skill from https://github.com/techwolf-ai/ai-first-toolkit/tree/main/plugins/tool-build-kit/skills/build-mcp into .github/skills/build-mcp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "build-mcp", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add techwolf-ai/ai-first-toolkit --skill build-mcp -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install techwolf-ai/ai-first-toolkit build-mcp --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/techwolf-ai/ai-first-toolkit.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/tool-build-kit/skills/build-mcp .opencode/skills/build-mcp && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "build-mcp" agent skill from https://github.com/techwolf-ai/ai-first-toolkit/tree/main/plugins/tool-build-kit/skills/build-mcp into .opencode/skills/build-mcp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "build-mcp", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
build-mcpBuild an MCP server end to end, tailored to how it will be used.
Build MCP is an agent skill from techwolf-ai/ai-first-toolkit. Build an MCP server end to end, tailored to how it will be used. Use when asked to build an MCP, create an MCP server, wrap an API as a tool, make a tool for Claude, expose a service to an agent, build a Claude connector, or turn a service into MCP tools. Asks up front who the server is for (just me, my org, or public) and what it wraps, then walks through analyze, build, deploy, scale, and distribute with steps tailored to that answer. Builds on the example-skills:mcp-builder skill for implementation depth.
Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `references/deploy-local.md`, `references/distribute-marketplace.md` and `references/node-sdk.md`).
It sits in Agent Workflows, covering MCP servers. It works with Model Context Protocol. The repository describes itself as: Open-source Claude Code skills and Codex skills for AI-first work. Audit, re-engineer, and bootstrap projects with AI-first design principles. The licence is MIT.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 2ee7841. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
claudenpxpythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Build MCP loads about 2.9k tokens when it runs, and up to ~8.5k if it reads all its reference files. Until then it costs about 131 tokens; SKILL.md has 1,546 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from techwolf-ai/ai-first-toolkit at commit 2ee7841, republished under its MIT licence (© techwolf-ai). 1,546 words, ~2,914 tokens.
.claude/skills/build-mcp/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.Build a Model Context Protocol (MCP) server the right way, end to end. The defining move of this skill: establish the user's context with AskUserQuestion before building anything, then tailor every phase to that context. A personal local server and a public hosted server share almost no steps past "build", so branch early and commit to the branch.
The Anthropic example-skills:mcp-builder skill is the gold-standard reference for the implementation itself: FastMCP and TypeScript SDK patterns, tool design, input/output schemas, annotations, error handling, and evaluation. Do not duplicate it. This skill is the scope-and-distribution wrapper around it: it decides what to build, for whom, where it runs, and how it ships. When you reach the build phase, invoke example-skills:mcp-builder for the deep implementation guidance and keep this skill's references thin.
Run them in order. The AskUserQuestion answers from Phase 0 gate phases 3, 4, and 5.
Before analyzing or writing anything, branch on the user's context. Ask the audience question first; it is the headline decision and it cascades into everything downstream. Then ask runtime only if it is still ambiguous, and ask language after Analyze (so you can recommend based on the wrapped service).
Question 1 (always, ask first): Audience / scope:
Use AskUserQuestion:
Question 2 (conditional): Where it runs:
Skip for "Just me" (assume local stdio). Ask for org/public when unclear:
Question 3 (after Analyze): Language:
Ask one question at a time. Confirm the resolved context back to the user in one line before proceeding (e.g. "Building a personal, local, Python stdio server that wraps the Linear API"). That resolved tuple drives the branch table below.
| Phase | Just me (local stdio) | My org (local stdio) | My org (hosted HTTP) | Public (package) | Public (hosted HTTP) |
|---|---|---|---|---|---|
| Deploy | claude mcp add --scope user or .mcp.json | Bundle in a Claude Code plugin; ${CLAUDE_PLUGIN_ROOT} paths | Deploy Streamable HTTP endpoint + OAuth/bearer | Publish to PyPI/npm; users run via uvx/npx | Deploy HTTP endpoint; document the URL |
| Scale | N/A (keep it maintainable) | N/A per-user; version the plugin | Real: statelessness, sessions, auth, rate limits | Versioning + backward-compat tool changes | Full: statelessness, auth, rate limits, observability |
| Distribute | Not shared. Stop after registration. | Org marketplace (.claude-plugin/marketplace.json + /plugin install) | Org marketplace entry pointing at the hosted URL | PyPI/npm + MCP registry via mcp-publisher | MCP registry remotes entry + public docs |
If a phase says N/A for the chosen branch, say so explicitly and move on. Do not pad it.
Reference files for each branch:
http/streamable-http naming gotcha.claude mcp add, scopes, .mcp.json, Claude Desktop config, uvx/npx run configs.Load only the references the current branch and phase need. Progressive disclosure.
Understand what you are wrapping before you write tools. Output a short tool plan, then confirm it.
readOnlyHint / destructiveHint annotations later.Deliverable: a numbered list of proposed tools, each with name, one-line purpose, inputs, read/write, and the service call it makes. Confirm with the user before building.
Hand off to the implementation reference for the chosen language, which in turn defers to mcp-builder for depth.
example-skills:mcp-builder for the full FastMCP guide.example-skills:mcp-builder for the full TypeScript SDK guide.Build to the tool plan from Phase 1. Apply mcp-builder's rules: clear tool names, Pydantic/Zod input schemas with descriptions and constraints, structured + human-readable output, pagination with limits, actionable error messages, and tool annotations. Compile and test with the MCP Inspector (npx @modelcontextprotocol/inspector) before moving on. Then write and run mcp-builder's evaluation set: about 10 realistic, read-only, verifiable questions in its XML format, scored with its scripts/evaluation.py harness (e.g. python scripts/evaluation.py -t stdio -c python -a server.py -o report.md evaluation.xml). Do not hand-wave this; a server that the eval can't drive is not done.
Start the server on stdio regardless of final runtime; it is the simplest thing to test locally. Switching to Streamable HTTP is a transport change at the end, not a rewrite (see transports.md).
Branch on the resolved runtime. Read references/deploy-local.md for stdio, references/transports.md for HTTP.
claude mcp add --scope user <name> -- <command> <args> for a personal server across all your projects, or a project-scoped .mcp.json. Verify with claude mcp list and /mcp. For org-local distribution, you do not register by hand on each machine; you bundle into a plugin (Phase 5).Origin header, bind to localhost when local, require auth. Connect with claude mcp add --transport http <name> <url> (add --header "Authorization: Bearer ..." for static tokens, or rely on the OAuth 401/WWW-Authenticate discovery flow). Containerize for repeatable deploys.Only substantive for hosted HTTP servers. For local/personal servers, state plainly that scaling is N/A and that the priority is maintainability and versioning, then skip to Distribute.
For hosted servers, read references/scaling.md and cover: stateless vs session-bearing design (Mcp-Session-Id), horizontal scaling, auth as an OAuth 2.1 resource server (validate token audience, never pass tokens through), least-privilege scopes, rate limiting, timeouts, observability, and protocol-version negotiation. Carry the caveat that there is no Anthropic-published "operate an MCP server" guide; this rests on the MCP spec plus normal infra practice.
The payoff phase. Branch hard on the audience answer. Read references/distribute-marketplace.md.
.claude-plugin/marketplace.json. The plugin ships the server via an mcpServers key in plugin.json or a bundled .mcp.json (use ${CLAUDE_PLUGIN_ROOT} for bundled paths). Colleagues run /plugin marketplace add <org>/<repo> then /plugin install <name>@<marketplace>. For auto-provisioning, add the marketplace to the project's .claude/settings.json under extraKnownMarketplaces. The TechWolf ai-first-toolkit repo is a working example of this layout.mcp-publisher CLI (init -> login github -> publish). For a hosted public server, register a remotes entry pointing at your URL instead of a package. Note the registry is in preview and its schema can change.You can do more than one (e.g. an org plugin and a public package). Distribution paths are additive.
© techwolf-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 6 other files (references) in plugins/tool-build-kit/skills/build-mcp of techwolf-ai/ai-first-toolkit.
Open the folder on GitHubat commit 2ee7841
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in techwolf-ai/ai-first-toolkit, which our catalogue first saw on October 7, 2026.
Build MCP next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Build MCP this skilltechwolf-ai/ai-first-toolkit | 132 | 1 repos | ~2.9k | Automated safety check: Pass | MIT | |
| MCP Server Builderanthropics/skills | 180k | 64 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| MCP Server BuildershareAI-lab/learn-claude-code | 78k | 5 repos | ~1.2k | Automated safety check: Pass | MIT | |
| MCP Integration for Pluginsanthropics/claude-plugins-official | 38k | 11 repos | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Fastmcp Client CLIPrefectHQ/fastmcp | 28k | 1 repos | ~823 | Automated safety check: Pass | Apache-2.0 | |
| Crush Configurationcharmbracelet/crush | 29k | — | ~3.7k | Automated safety check: Pass | Custom licence |
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
shareAI-lab/learn-claude-code
Walks through building MCP servers in Python or TypeScript that expose tools, resources and prompts to Claude, with templates, registration and testing.
anthropics/claude-plugins-official
Explains how to bundle Model Context Protocol servers in a Claude Code plugin, covering config files, stdio, SSE, HTTP and WebSocket server types, and authentication.
PrefectHQ/fastmcp
Query and invoke tools on MCP servers using fastmcp list and fastmcp call.
charmbracelet/crush
Explains how to configure the Crush coding agent with crushrc or crush.json, covering providers, models, LSPs, MCP servers, hooks, permissions and config precedence.
mksglu/context-mode
Routes large command, file, API and browser output through context-mode tools so only the needed result enters the agent's context, instead of dumping it via Bash.
techwolf-ai/ai-first-toolkit
Mine the user's Claude Code + Cowork session history into a structured task profile, what they do with AI, how often, how successfully where friction lives, then propose atomic skills that would…
techwolf-ai/ai-first-toolkit
Find context from past Claude Code (CLI) and Claude Cowork (desktop) sessions on this Mac.
techwolf-ai/ai-first-toolkit
Personal diagnosis of where your Claude Code + Cowork spend goes.
techwolf-ai/ai-first-toolkit
Write or develop a blog post. An agent skill from techwolf-ai/ai-first-toolkit.
techwolf-ai/ai-first-toolkit
Write or develop an opinion piece (opiniestuk/op-ed). An agent skill from techwolf-ai/ai-first-toolkit.
techwolf-ai/ai-first-toolkit
Analyze, re-engineer, or bootstrap projects to align with AI-first design principles.
Works with
Categories
Build an MCP server end to end, tailored to how it will be used. Build MCP is an agent skill from techwolf-ai/ai-first-toolkit. Build an MCP server end to end, tailored to how it will be used.
Build MCP fits situations like: asked to build an MCP; create an MCP server; wrap an API as a tool; make a tool for Claude.
Run `npx skills add techwolf-ai/ai-first-toolkit --skill build-mcp -a claude-code`. Or copy the skill folder (plugins/tool-build-kit/skills/build-mcp in techwolf-ai/ai-first-toolkit) into .claude/skills/build-mcp in your project. Claude Code loads it when a task matches its description.
Run `npx skills add techwolf-ai/ai-first-toolkit --skill build-mcp -a codex`. Or copy the skill folder (plugins/tool-build-kit/skills/build-mcp in techwolf-ai/ai-first-toolkit) into .agents/skills/build-mcp in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add techwolf-ai/ai-first-toolkit --skill build-mcp -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/build-mcp, .gemini/skills/build-mcp, .github/skills/build-mcp and .opencode/skills/build-mcp in your project.
Going by SKILL.md and its folder, Build MCP needs the command-line tools its instructions call (claude, npx and python). Our summary lists: Python 3; Node.js.
SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Build MCP is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.6k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Build MCP: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Fastmcp Client CLI (PrefectHQ/fastmcp, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
techwolf-ai (a GitHub organization) maintains it in techwolf-ai/ai-first-toolkit, which has 132 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on September 29, 2026.
Source: techwolf-ai/ai-first-toolkit on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.