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SKILL verified MIT Self-run

Ls Mcp Builder

skill-cruxexperts-localsetup-ls-mcp-builder · by CruxExperts

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (MCPServer) or Node/TypeScript (MCP SDK).

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Install

$ agentstack add skill-cruxexperts-localsetup-ls-mcp-builder

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

Verified badge

Passed review? Show it. Paste this badge into your README, it links to the public security report.

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[![AgentStack Verified](https://agentstack.voostack.com/badges/verified.svg)](https://agentstack.voostack.com/security/report/skill-cruxexperts-localsetup-ls-mcp-builder)

Reliability & compatibility

✓ Security review passed
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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

MCP Server Development Guide

Use this skill when designing, implementing, or evaluating an MCP server for an external API or service. Keep the main workflow short, then load the detailed reference that matches the implementation language or evaluation task.

Load First

  • MCP protocol: fetch https://modelcontextprotocol.io/llms-full.txt
  • [MCP best practices](./references/mcpbestpractices.md)
  • Python SDK docs: fetch https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md
  • TypeScript SDK docs: fetch https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md

Workflow

1. Research and Plan

  • Read the target service API docs: auth, rate limits, pagination, errors,

endpoints, schemas, and destructive operations.

  • Design workflow-level tools instead of thin endpoint wrappers.
  • Prefer high-signal outputs, stable human-readable IDs, and explicit

concise or detailed response modes.

  • Plan shared request helpers, pagination, formatting, input validation,

authentication, and actionable error messages before writing tools.

  • Mark read-only, destructive, idempotent, and open-world behavior with MCP tool

annotations where supported.

2. Implement

For Python, load [Python implementation guide](./references/pythonmcpserver.md). Use the MCP Python SDK, Pydantic models, type hints, async I/O for external calls, shared helpers, and clear module-level constants.

For Node/TypeScript, load [TypeScript implementation guide](./references/nodemcpserver.md). Use the MCP TypeScript SDK, strict TypeScript, Zod schemas, explicit return types, and a working build script.

For every tool:

  • Use schema validation with useful constraints and examples.
  • Write descriptions that explain when to use the tool, expected inputs,

output shape, and recovery steps for common errors.

  • Keep tool outputs bounded and predictable; paginate or summarize large data.
  • Return errors as actionable tool results when the agent can recover.

3. Review and Test

  • Check for duplicated code, inconsistent output formats, weak validation, and

generic exceptions.

  • Do not run long-lived stdio MCP servers directly in the main terminal without

a timeout or harness.

  • Python smoke checks: python -m py_compile server.py, then run the evaluation

harness or a short timeout test.

  • TypeScript smoke checks: npm run build, confirm dist/ output, then run the

evaluation harness or a short timeout test.

  • Use the quality checklist in the language guide before handing off.

4. Evaluate

Load [evaluation guide](./references/evaluation.md) to create read-only, verifiable questions and run the bundled harness.

Run the harness with its dependencies from the skill directory:

uv run --with 'mcp>=2,=0.39.0' --with 'openai>=1.0.0' -- python scripts/evaluation.py --help

Provider examples:

# Claude provider
ANTHROPIC_API_KEY=... uv run --with 'mcp>=2,=0.39.0' --with 'openai>=1.0.0' -- python scripts/evaluation.py \
  -t stdio -c python -a my_server.py evaluation.xml

# OpenAI-compatible provider
OPENAI_API_KEY=... uv run --with 'mcp>=2,=0.39.0' --with 'openai>=1.0.0' -- python scripts/evaluation.py --provider openai \
  -t stdio -c python -a my_server.py evaluation.xml

# Emulation provider, no LLM key
uv run --with 'mcp>=2,
  
    Question that requires read-only tool use.
    Stable expected answer.
  

Reference Library

  • [MCP best practices](./references/mcpbestpractices.md): universal design,

naming, response, pagination, security, and error-handling guidance.

  • [Python implementation guide](./references/pythonmcpserver.md): MCPServer

examples, Pydantic patterns, resources, prompts, and quality checklist.

  • [TypeScript implementation guide](./references/nodemcpserver.md): SDK server

structure, Zod patterns, tool registration, build setup, and checklist.

  • [Evaluation guide](./references/evaluation.md): question design, XML format,

harness setup, providers, reporting, and troubleshooting.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.