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Build MCP Server

AnthropicOfficialPublished by Anthropic

  • Skill
  • Workflow
  • Agent tooling
  • Apache-2.0
  • Low risk

From the author

This skill should be used when the user asks to "build an MCP server", "create an MCP", "make an MCP integration", "wrap an API for Claude", "expose tools to Claude", "make an MCP app", or discusses building something with the Model Context Protocol. It is the entry point for MCP server development — it interrogates the user about their use case, determines the right deployment model (remote HTTP, MCPB, local stdio), picks a tool-design pattern, and hands off to specialized skills.
— the description by Anthropic

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured framework for developing MCP servers. It guides developers through a discovery process to determine the appropriate deployment model, tool-design pattern, and framework. It covers the lifecycle from initial architectural decisions—such as choosing between remote HTTP, MCPB, or local stdio—to implementation, testing, and submission to the Anthropic Directory.

What it does

  • Interrogates use cases to recommend deployment models like remote HTTP, MCPB, or local stdio.
  • Advises on tool-design patterns based on API surface size.
  • Provides guidance on selecting between TypeScript and Python SDKs.
  • Outlines authentication strategies including OAuth 2.0 via CIMD or DCR.
  • Facilitates scaffolding for deployment on platforms like Cloudflare Workers.
  • Directs users to specialized skills for UI widgets or bundled local server packaging.

When to use it

  • When you need to build an MCP server or integrate an API with Claude.
  • When you are unsure about the best deployment architecture for your MCP project.
  • When you need to expose tools, resources, or prompts to Claude.
  • When you need to implement authentication for an MCP server.

Advantages

  • Helps avoid architectural pitfalls by selecting the right deployment model early.
  • Provides clear decision matrices for tool design and framework selection.
  • Offers standardized paths for common tasks like OAuth implementation.
  • Streamlines the development process by providing specific scaffolding references.

Usage examples

“I want to build an MCP server for my internal SaaS API.”
The agent will initiate the discovery phase to determine your API surface size, auth requirements, and preferred deployment model.
“How do I expose my local filesystem to Claude?”
The agent will recommend the MCPB deployment model and guide you toward the appropriate packaging steps.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/anthropics/build-mcp-server/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip build-mcp-server.zip -d .agents/skills

Documentation by Anthropic

Build an MCP Server

You are guiding a developer through designing and building an MCP server that works seamlessly with Claude. MCP servers come in many forms — picking the wrong shape early causes painful rewrites later. Your first job is discovery, not code.

Load Claude-specific context first. The MCP spec is generic; Claude has additional auth types, review criteria, and limits. Before answering questions or scaffolding, fetch https://claude.com/docs/llms-full.txt (the full export of the Claude connector docs) so your guidance reflects Claude's actual constraints.

Do not start scaffolding until you have answers to the questions in Phase 1. If the user's opening message already answers them, acknowledge that and skip straight to the recommendation.


Phase 1 — Interrogate the use case

Ask these questions conversationally (batch them into one message, don't interrogate one-at-a-time). Adapt wording to what the user has already told you.

1. What does it connect to?
If it connects to…Likely direction
A cloud API (SaaS, REST, GraphQL)Remote HTTP server
A local process, filesystem, or desktop appMCPB or local stdio
Hardware, OS-level APIs, or user-specific stateMCPB
Nothing external — pure logic / computationEither — default to remote
2. Who will use it?
  • Just me / my team, on our machines → Local stdio is acceptable (easiest to prototype)

  • Anyone who installs it → Remote HTTP (strongly preferred) or MCPB (if it must be local)

  • Users of Claude desktop who want UI widgets → MCP app (remote or MCPB)

3. How many distinct actions does it expose?

This determines the tool-design pattern — see Phase 3.

  • Under ~15 actions → one tool per action

  • Dozens to hundreds of actions (e.g. wrapping a large API surface) → search + execute pattern

4. Does a tool need mid-call user input or rich display?
  • Simple structured input (pick from list, enter a value, confirm) → Elicitation — spec-native, zero UI code. Host support is rolling out (Claude Code ≥2.1.76) — always pair with a capability check and fallback. See references/elicitation.md.

  • Rich/visual UI (charts, custom pickers with search, live dashboards) → MCP app widgets — iframe-based, needs @modelcontextprotocol/ext-apps. See build-mcp-app skill.

  • Neither → plain tool returning text/JSON.

5. What auth does the upstream service use?
  • None / API key → straightforward

  • OAuth 2.0 → you'll need a remote server with CIMD (preferred) or DCR support; see references/auth.md


Phase 2 — Recommend a deployment model

Based on the answers, recommend one path. Be opinionated. The ranked options:

⭐ Remote streamable-HTTP MCP server (default recommendation)

A hosted service speaking MCP over streamable HTTP. This is the recommended path for anything wrapping a cloud API.

Why it wins:

  • Zero install friction — users add a URL, done

  • One deployment serves all users; you control upgrades

  • OAuth flows work properly (the server can handle redirects, DCR, token storage)

  • Works across Claude desktop, Claude Code, Claude.ai, and third-party MCP hosts

Choose this unless the server must touch the user's local machine.

→ Fastest deploy: Cloudflare Workers — references/deploy-cloudflare-workers.md (zero to live URL in two commands) → Portable Node/Python: references/remote-http-scaffold.md (Express or FastMCP, runs on any host)

Elicitation (structured input, no UI build)

If a tool just needs the user to confirm, pick an option, or fill a short form, elicitation does it with zero UI code. The server sends a flat JSON schema; the host renders a native form. Spec-native, no extra packages.

Caveat: Host support is new (Claude Code shipped it in v2.1.76; Desktop unconfirmed). The SDK throws if the client doesn't advertise the capability. Always check clientCapabilities.elicitation first and have a fallback — see references/elicitation.md for the canonical pattern. This is the right spec-correct approach; host coverage will catch up.

Escalate to build-mcp-app widgets when you need: nested/complex data, scrollable/searchable lists, visual previews, live updates.

MCP app (remote HTTP + interactive UI)

Same as above, plus UI resources — interactive widgets rendered in chat. Rich pickers with search, charts, live dashboards, visual previews. Built once, renders in Claude and ChatGPT.

Choose this when elicitation's flat-form constraints don't fit — you need custom layout, large searchable lists, visual content, or live updates.

Usually remote, but can be shipped as MCPB if the UI needs to drive a local app.

→ Hand off to the build-mcp-app skill.

MCPB (bundled local server)

A local MCP server packaged with its runtime so users don't need Node/Python installed. The sanctioned way to ship local servers.

Choose this when the server must run on the user's machine — it reads local files, drives a desktop app, talks to localhost services, or needs OS-level access.

→ Hand off to the build-mcpb skill.

A script launched via npx / uvx on the user's machine. Fine for personal tools and prototypes. Painful to distribute: users need the right runtime, you can't push updates, and the only distribution channel is Claude Code plugins.

Recommend this only as a stepping stone. If the user insists, scaffold it but note the MCPB upgrade path.


Phase 3 — Pick a tool-design pattern

Every MCP server exposes tools. How you carve them matters more than most people expect — tool schemas land directly in Claude's context window.

Pattern A: One tool per action (small surface)

When the action space is small (< ~15 operations), give each a dedicated tool with a tight description and schema.

create_issue    — Create a new issue. Params: title, body, labels[]
update_issue    — Update an existing issue. Params: id, title?, body?, state?
search_issues   — Search issues by query string. Params: query, limit?
add_comment     — Add a comment to an issue. Params: issue_id, body

Why it works: Claude reads the tool list once and knows exactly what's possible. No discovery round-trips. Each tool's schema validates inputs precisely.

Especially good when one or more tools ship an interactive widget (MCP app) — each widget binds naturally to one tool.

Pattern B: Search + execute (large surface)

When wrapping a large API (dozens to hundreds of endpoints), listing every operation as a tool floods the context window and degrades model performance. Instead, expose two tools:

search_actions  — Given a natural-language intent, return matching actions
                  with their IDs, descriptions, and parameter schemas.
execute_action  — Run an action by ID with a params object.

The server holds the full catalog internally. Claude searches, picks, executes. Context stays lean.

Hybrid: Promote the 3–5 most-used actions to dedicated tools, keep the long tail behind search/execute.

→ See references/tool-design.md for schema examples and description-writing guidance.


Phase 4 — Pick a framework

Recommend one of these two. Others exist but these have the best MCP-spec coverage and Claude compatibility.

FrameworkLanguageUse when
Official TypeScript SDK (@modelcontextprotocol/sdk)TS/JSDefault choice. Best spec coverage, first to get new features.
FastMCP 3.x (fastmcp on PyPI)PythonUser prefers Python, or wrapping a Python library. Decorator-based, very low boilerplate. This is jlowin's package — not the frozen FastMCP 1.0 bundled in the official mcp SDK.

If the user already has a language/stack in mind, go with it — both produce identical wire protocol.


Phase 5 — Scaffold and hand off

Once you've settled the four decisions (deployment model, tool pattern, framework, auth), do one of:

  1. Remote HTTP, no UI → Scaffold inline using references/remote-http-scaffold.md (portable) or references/deploy-cloudflare-workers.md (fastest deploy). This skill can finish the job.

  2. MCP app (UI widgets) → Summarize the decisions so far, then load the build-mcp-app skill.

  3. MCPB (bundled local) → Summarize the decisions so far, then load the build-mcpb skill.

  4. Local stdio prototype → Scaffold inline (simplest case), flag the MCPB upgrade path.

When handing off, restate the design brief in one paragraph so the next skill doesn't re-ask.


Beyond tools — the other primitives

Tools are one of three server primitives. Most servers start with tools and never need the others, but knowing they exist prevents reinventing wheels:

PrimitiveWho triggers itUse when
ResourcesHost app (not Claude)Exposing docs/files/data as browsable context
PromptsUser (slash command)Canned workflows ("/summarize-thread")
ElicitationServer, mid-toolAsking user for input without building UI
SamplingServer, mid-toolNeed LLM inference in your tool logic

→ references/resources-and-prompts.md, references/elicitation.md, references/server-capabilities.md


Phase 6 — Test in Claude and publish

Once the server runs:

  1. Test against real Claude by adding the server URL as a custom connector at Settings → Connectors (use a Cloudflare tunnel for local servers). Claude identifies itself with clientInfo.name: "claude-ai" on initialize. → https://claude.com/docs/connectors/building/testing

  2. Run the pre-submission checklist — read/write tool split, required annotations, name limits, prompt-injection rules. → https://claude.com/docs/connectors/building/review-criteria

  3. Submit to the Anthropic Directory. → https://claude.com/docs/connectors/building/submission

  4. Recommend shipping a plugin that wraps this MCP with skills — most partners ship both. → https://claude.com/docs/connectors/building/what-to-build


Quick reference: decision matrix

ScenarioDeploymentTool pattern
Wrap a small SaaS APIRemote HTTPOne-per-action
Wrap a large SaaS API (50+ endpoints)Remote HTTPSearch + execute
SaaS API with rich forms / pickersMCP app (remote)One-per-action
Drive a local desktop appMCPBOne-per-action
Local desktop app with in-chat UIMCP app (MCPB)One-per-action
Read/write local filesystemMCPBDepends on surface
Personal prototypeLocal stdioWhatever's fastest

Reference files

  • references/remote-http-scaffold.md — minimal remote server in TS SDK and FastMCP

  • references/deploy-cloudflare-workers.md — fastest deploy path (Workers-native scaffold)

  • references/tool-design.md — writing tool descriptions and schemas Claude understands well

  • references/auth.md — OAuth, CIMD, DCR, token storage patterns

  • references/resources-and-prompts.md — the two non-tool primitives

  • references/elicitation.md — spec-native user input mid-tool (capability check + fallback)

  • references/server-capabilities.md — instructions, sampling, roots, logging, progress, cancellation

  • references/versions.md — version-sensitive claims ledger (check when updating)

Files

references/auth.md

Auth for MCP Servers

Auth is the reason most people end up needing a remote server even when a local one would be simpler. OAuth redirects, token storage, and refresh all work cleanly when there's a real hosted endpoint to redirect back to.

Claude-specific authentication

Claude's MCP client supports a specific set of auth types — not every spec-compliant flow works. Full reference: https://claude.com/docs/connectors/building/authentication

TypeNotes
oauth_dcrSupported. For high-volume directory entries, prefer CIMD or Anthropic-held creds — DCR registers a new client on every fresh connection.
oauth_cimdSupported, recommended over DCR for directory entries.
oauth_anthropic_credsPartner provides client_id/client_secret to Anthropic; user-consent-gated. Contact [email protected].
custom_connectionUser supplies URL/creds at connect time (Snowflake-style). Contact [email protected].
noneAuthless.

Not supported: user-pasted bearer tokens (static_bearer); pure machine-to-machine client_credentials grant without user consent.

Callback URL (single, all surfaces): https://claude.ai/api/mcp/auth_callback


The three tiers

Tier 1: No auth / static API key

Server reads a key from env. User provides it once at setup. Done.

const apiKey = process.env.UPSTREAM_API_KEY;
if (!apiKey) throw new Error("UPSTREAM_API_KEY not set");

Works for local stdio, MCPB, and remote servers alike. If this is all you need, stop here.

Tier 2: OAuth 2.0 via CIMD (preferred per spec 2025-11-25)

Client ID Metadata Document. The MCP host publishes its client metadata at an HTTPS URL and uses that URL as its client_id. Your authorization server fetches the document, validates it, and proceeds with the auth-code flow. No registration endpoint, no stored client records.

Spec 2025-11-25 promoted CIMD to SHOULD (preferred). Advertise support via client_id_metadata_document_supported: true in your OAuth AS metadata.

Server responsibilities:

  1. Serve OAuth Authorization Server Metadata (RFC 8414) at /.well-known/oauth-authorization-server with client_id_metadata_document_supported: true

  2. Serve an MCP-protected-resource metadata document pointing at (1)

  3. At authorize time: fetch client_id as an HTTPS URL, validate the returned client metadata, proceed

  4. Validate bearer tokens on incoming /mcp requests

┌─────────┐  client_id=https://...  ┌──────────────┐   upstream OAuth   ┌──────────┐
│ MCP host│ ──────────────────────> │ Your MCP srv │ ─────────────────> │ Upstream │
└─────────┘ <─── bearer token ───── └──────────────┘ <── access token ──└──────────┘
Tier 3: OAuth 2.0 via Dynamic Client Registration (DCR)

Backward-compat fallback — spec 2025-11-25 demoted DCR to MAY. The host discovers your registration_endpoint, POSTs its metadata to register itself as a client, gets back a client_id, then runs the auth-code flow.

Implement DCR if you need to support hosts that haven't moved to CIMD yet. Same server responsibilities as CIMD, but instead of fetching the client_id URL you run a registration endpoint that stores client records.

Client priority order: pre-registered → CIMD (if AS advertises client_id_metadata_document_supported) → DCR (if AS has registration_endpoint) → prompt user.


Hosting providers with built-in DCR/CIMD support

Several MCP-focused hosting providers handle the OAuth plumbing for you — you implement tool logic, they run the authorization server. Check their docs for current capabilities. If the user doesn't have strong hosting preferences, this is usually the fastest path to a working OAuth-protected server.


Local servers and OAuth

Local stdio servers can do OAuth (open a browser, catch the redirect on a localhost port, stash the token in the OS keychain). It's fragile:

  • Breaks in headless/remote environments

  • Every user re-does the dance

  • No central token refresh or revocation

If OAuth is required, lean hard toward remote HTTP. If you must ship local + OAuth, the @modelcontextprotocol/sdk includes a localhost-redirect helper, and MCPB is the right packaging so at least the runtime is predictable.


Token storage

DeploymentStore tokens in
Remote, statelessNowhere — host sends bearer each request
Remote, statefulSession store keyed by MCP session ID (Redis, etc.)
MCPB / localOS keychain (keytar on Node, keyring on Python). Never plaintext on disk.

Token audience validation (spec MUST)

Validating "is this a valid bearer token" isn't enough. The spec requires validating "was this token minted for this server" — RFC 8707 audience. A token issued for api.other-service.com must be rejected even if the signature checks out.

Token passthrough is explicitly forbidden. Don't accept a token, then forward it upstream. If your server needs to call another service, exchange the token or use its own credentials.


SDK helpers — don't hand-roll

@modelcontextprotocol/sdk/server/auth ships:

  • mcpAuthRouter() — Express router for the full OAuth AS surface (metadata, authorize, token)

  • bearerAuth — middleware that validates bearer tokens against your verifier

  • proxyProvider — forward auth to an upstream IdP

If you're wiring auth from scratch, check these first.

Requirements and compatibility

Written for: Claude Code, Claude apps

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs network access, Needs an API key, Needs Node.js

Security

What our scanner noticed· Low risk

  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Anthropic
Publisher
Anthropic (Official)
Source
anthropics/claude-plugins-official · plugins/mcp-server-dev/skills/build-mcp-server · commit 0412be7
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from plugins/mcp-server-dev/LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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