This skill should be used when the user wants to "package an MCP server", "bundle an MCP", "make an MCPB", "ship a local MCP server", "distribute a local MCP", discusses ".mcpb files", mentions bundling a Node or Python runtime with their MCP server, or needs an MCP server that interacts with the local filesystem, desktop apps, or OS and must be installable without the user having Node/Python set up.
复制安装指令,让 AI 自动完成配置 · 推荐新手
请帮我安装 askskill 上的 "build-mcpb" 技能: 1. 下载 https://raw.githubusercontent.com/anthropics/claude-plugins-official/main/plugins/mcp-server-dev/skills/build-mcpb/SKILL.md 2. 保存为 ~/.claude/skills/build-mcpb/SKILL.md 3. 装好后重载技能,告诉我可以用了
MCPB is a local MCP server packaged with its runtime. The user installs one file; it runs without needing Node, Python, or any toolchain on their machine. It's the sanctioned way to distribute local MCP servers.
MCPB is the secondary distribution path. Anthropic recommends remote MCP servers for directory listing — see https://claude.com/docs/connectors/building/what-to-build.
Use MCPB when the server must run on the user's machine — reading local files, driving a desktop app, talking to localhost services, OS-level APIs. If your server only hits cloud APIs, you almost certainly want a remote HTTP server instead (see build-mcp-server). Don't pay the MCPB packaging tax for something that could be a URL.
my-server.mcpb (zip archive)
├── manifest.json ← identity, entry point, config schema, compatibility
├── server/ ← your MCP server code
│ ├── index.js
│ └── node_modules/ ← bundled dependencies (or vendored)
└── icon.png
The host reads manifest.json, launches server.mcp_config.command as a stdio MCP server, and pipes messages. From your code's perspective it's identical to a local stdio server — the only difference is packaging.
{
"$schema": "https://raw.githubusercontent.com/anthropics/mcpb/main/schemas/mcpb-manifest-v0.4.schema.json",
"manifest_version": "0.4",
"name": "local-files",
"version": "0.1.0",
"description": "Read, search, and watch files on the local filesystem.",
"author": { "name": "Your Name" },
"server": {
"type": "node",
"entry_point": "server/index.js",
"mcp_config": {
"command": "node",
"args": ["${__dirname}/server/index.js"],
"env": {
"ROOT_DIR": "${user_config.rootDir}"
}
}
},
"user_config": {
"rootDir": {
"type": "directory",
"title": "Root directory",
"description": "Directory to expose. Defaults to ~/Documents.",
"default": "${HOME}/Documents",
"required": true
}
},
"compatibility": {
"claude_desktop": ">=1.0.0",
"platforms": ["darwin", "win32", "linux"]
}
}
server.type — node, python, or binary. Informational; the actual launch comes from mcp_config.
server.mcp_config — the literal command/args/env to spawn. Use ${__dirname} for bundle-relative paths and ${user_config.<key>} to substitute install-time config. There's no auto-prefix — the env var names your server reads are exactly what you put in env.
user_config — install-time settings surfaced in the host's UI. type: "directory" renders a native folder picker. sensitive: true stores in OS keychain. See references/manifest-schema.md for all fields.
The server itself is a standard stdio MCP server. Nothing MCPB-specific in the tool logic.
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";
import { readFile, readdir } from "node:fs/promises";
import { join } from "node:path";
import { homedir } from "node:os";
// ROOT_DIR comes from what you put in manifest's server.mcp_config.env — no auto-prefix
const ROOT = (process.env.ROOT_DIR ?? join(homedir(), "Documents"));
const server = new McpServer({ name: "local-files", version: "0.1.0" });
server.registerTool(
"list_files",
{
description: "List files in a directory under the configured root.",
inputSchema: { path: z.string().default(".") },
annotations: { readOnlyHint: true },
},
async ({ path }) => {
const entries = await readdir(join(ROOT, path), { withFileTypes: true });
const list = entries.map(e => ({ name: e.name, dir: e.isDirectory() }));
return { content: [{ type: "text", text: JSON.stringify(list, null, 2) }] };
},
);
…
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An example user-invoked skill that demonstrates frontmatter options and the skills/<name>/SKILL.md layout
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