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.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionbuild-mcp-serverExecute the skills CLI command in your project's root directory to begin installation:
Fetches build-mcp-server from anthropics/claude-plugins-official and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate build-mcp-server. Access via /build-mcp-server in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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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.
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.
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.
| If it connects to… | Likely direction |
|---|---|
| A cloud API (SaaS, REST, GraphQL) | Remote HTTP server |
| A local process, filesystem, or desktop app | MCPB or local stdio |
| Hardware, OS-level APIs, or user-specific state | MCPB |
| Nothing external — pure logic / computation | Either — default to remote |
This determines the tool-design pattern — see Phase 3.
references/elicitation.md.@modelcontextprotocol/ext-apps. See build-mcp-app skill.references/auth.mdBased on the answers, recommend one path. Be opinionated. The ranked options:
A hosted service speaking MCP over streamable HTTP. This is the recommended path for anything wrapping a cloud API.
Why it wins:
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)
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.
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.
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.
Every MCP server exposes tools. How you carve them matters more than most people expect — tool schemas land directly in Claude's context window.
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.
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.
Recommend one of these two. Others exist but these have the best MCP-spec coverage and Claude compatibility.
| Framework | Language | Use when |
|---|---|---|
Official TypeScript SDK (@modelcontextprotocol/sdk) |
TS/JS | Default choice. Best spec coverage, first to get new features. |
FastMCP 3.x (fastmcp on PyPI) |
Python | User 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.
Once you've settled the four decisions (deployment model, tool pattern, framework, auth), do one of:
references/remote-http-scaffold.md (portable) or references/deploy-cloudflare-workers.md (fastest deploy). This skill can finish the job.build-mcp-app skill.build-mcpb skill.When handing off, restate the design brief in one paragraph so the next skill doesn't re-ask.
Tools are one of three server primitives. Most servers start with tools and never need the others, but knowing they exist prevents reinventing wheels:
| Primitive | Who triggers it | Use when |
|---|---|---|
| Resources | Host app (not Claude) | Exposing docs/files/data as browsable context |
| Prompts | User (slash command) | Canned workflows ("/summarize-thread") |
| Elicitation | Server, mid-tool | Asking user for input without building UI |
| Sampling | Server, mid-tool | Need LLM inference in your tool logic |
→ references/resources-and-prompts.md, references/elicitation.md, references/server-capabilities.md
| Scenario | Deployment | Tool pattern |
|---|---|---|
| Wrap a small SaaS API | Remote HTTP | One-per-action |
| Wrap a large SaaS API (50+ endpoints) | Remote HTTP | Search + execute |
| SaaS API with rich forms / pickers | MCP app (remote) | One-per-action |
| Drive a local desktop app | MCPB | One-per-action |
| Local desktop app with in-chat UI | MCP app (MCPB) | One-per-action |
| Read/write local filesystem | MCPB | Depends on surface |
| Personal prototype | Local stdio | Whatever's fastest |
references/remote-http-scaffold.md — minimal remote server in TS SDK and FastMCPreferences/deploy-cloudflare-workers.md — fastest deploy path (Workers-native scaffold)references/tool-design.md — writing tool descriptions and schemas Claude understands wellreferences/auth.md — OAuth, CIMD, DCR, token storage patternsreferences/resources-and-prompts.md — the two non-tool primitivesreferences/elicitation.md — spec-native user input mid-tool (capability check + fallback)references/server-capabilities.md — instructions, sampling, roots, logging, progress, cancellationreferences/versions.md — version-sensitive claims ledger (check when updating)Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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build-mcp-server reduced setup friction for our internal harness; good balance of opinion and flexibility.
build-mcp-server fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
build-mcp-server reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added build-mcp-server from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in build-mcp-server — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added build-mcp-server from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
build-mcp-server has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend build-mcp-server for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
build-mcp-server is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend build-mcp-server for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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