build-mcpb▌
anthropics/claude-plugins-official · updated Apr 8, 2026
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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.
Build an MCPB (Bundled Local MCP Server)
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.
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.
What an MCPB bundle contains
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.
Manifest
{
"$schema": "https://raw.githubusercontent.com/anthropics/mcpb/main/schemas/mcpb-manifest-v0.4.schema.json",
"manifest_version": "0.4",
"name": "local-files",
"version": "0.1.0",
"description": "Read, search, and watch files on the local filesystem.",
"author": { "name": "Your Name" },
"server": {
"type": "node",
"entry_point": "server/index.js",
"mcp_config": {
"command": "node",
"args": ["${__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.
Server code: same as local stdio
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) }] };
},
);
server.registerTool(
"read_file",
{
description: "Read a file's contents. Path is relative to the configured root.",
inputSchema: { path: z.string() },
annotations: { readOnlyHint: true },
},
async ({ path }) => {
const text = await readFile(join(ROOT, path), "utf8");
return { content: [{ type: "text", text }] };
},
);
const transport = new StdioServerTransport();
await server.connect(transport);
Sandboxing is entirely your job. There is no manifest-level sandbox — the process runs with full user privileges. Validate paths, refuse to escape ROOT, allowlist spawns. See references/local-security.md.
Before hardcoding ROOT from a config env var, check if the host supports roots/list — the spec-native way to get user-approved directories. See references/local-security.md for the pattern.
Build pipeline
Node
npm install
npx esbuild src/index.ts --bundle --platform=node --outfile=server/index.js
# or: copy node_modules wholesale if native deps resist bundling
npx @anthropic-ai/mcpb pack
mcpb pack zips the directory and validates manifest.json against the schema.
Python
pip install -t server/vendor -r requirements.txt
npx @anthropic-ai/mcpb pack
Vendor dependencies into a subdirectory and prepend it to sys.path in your entry script. Native extensions (numpy, etc.) must be built for each target platform — avoid native deps if you can.
MCPB has no sandbox — security is on you
Unlike mobile app stores, MCPB does NOT enforce permissions. The manifest has no permissions block — the server runs with full user privileges. references/local-security.md is mandatory reading, not optional. Every path must be validated, every spawn must be allowlisted, because nothing stops you at the platform level.
If you came here expecting filesystem/network scoping from the manifest: it doesn't exist. Build it yourself in tool handlers.
If your server's only job is hitting a cloud API, stop — that's a remote server wearing an MCPB costume. The user gains nothing from running it locally, and you're taking on local-security burden for no reason.
MCPB + UI widgets
MCPB servers can serve UI resources exactly like remote MCP apps — the widget mechanism is transport-agnostic. A local file picker that browses the actual disk, a dialog that controls a native app, etc.
Widget authoring is covered in the build-mcp-app skill; it works the same here. The only difference is where the server runs.
Testing
# Interactive manifest creation (first time)
npx @anthropic-ai/mcpb init
# Run the server directly over stdHow to use build-mcpb on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add build-mcpb
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches build-mcpb from GitHub repository anthropics/claude-plugins-official and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate build-mcpb. Access the skill through slash commands (e.g., /build-mcpb) or your agent's skill management interface.
Security & Verification Notice
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.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ 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.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★36 reviews- ★★★★★Benjamin Garcia· Dec 16, 2024
build-mcpb has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Pratham Ware· Dec 8, 2024
Registry listing for build-mcpb matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Hiroshi Agarwal· Dec 4, 2024
build-mcpb fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Evelyn Okafor· Nov 23, 2024
We added build-mcpb from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Emma Malhotra· Nov 15, 2024
Registry listing for build-mcpb matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Noor Menon· Nov 7, 2024
build-mcpb reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Emma Kapoor· Oct 26, 2024
I recommend build-mcpb for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Zaid Abbas· Oct 14, 2024
Solid pick for teams standardizing on skills: build-mcpb is focused, and the summary matches what you get after install.
- ★★★★★Arya Singh· Oct 6, 2024
Keeps context tight: build-mcpb is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Yash Thakker· Sep 25, 2024
Solid pick for teams standardizing on skills: build-mcpb is focused, and the summary matches what you get after install.
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