Install
$ agentstack add skill-cruxexperts-localsetup-ls-mcp-builder ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo 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.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →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, confirmdist/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.
- Author: CruxExperts
- Source: CruxExperts/localsetup
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.