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Mcp server builder

Skill yeaight7/agent-powerups/skills/mcp-server-builder

Design high-quality MCP servers around workflows, narrow schemas, context-aware outputs, and actionable errors. Use when building or reviewing MCP tools for real agent tasks.From its SKILL.md

Install
npx -y skills add yeaight7/agent-powerups --skill mcp-server-builder

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  • 6 stars6 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

3.5 KB, 731 tokens by cl100k_base, as published. Nobody here has run it

MCP Server Builder

Use this skill when designing or implementing an MCP server.

When to Use

  • Building a new MCP server from scratch.
  • Refactoring a weak or over-thin MCP tool surface.
  • Reviewing whether a server exposes the right workflows.
  • Designing evaluation cases for MCP usability.

Core Principles

  • Build workflow tools, not thin endpoint wrappers — one tool should complete a meaningful agent task, not expose a single API method.
  • Keep input schemas narrow and typed — reject unknown fields; use enums over free strings where possible.
  • Return high-signal, size-bounded outputs — default to concise; add detail or verbose flags when larger payloads are occasionally useful.
  • Make error messages corrective — tell the agent what to do next, not just what went wrong.
  • Prefer human-meaningful identifiers over opaque IDs when both are available.
  • Design evaluation cases before declaring the server "done".

Workflow

1. Map the workflow

  • Identify the real tasks an agent must complete, not the underlying API surface.
  • Merge low-level steps into meaningful operations (e.g., one create_and_publish tool instead of separate create, validate, publish).

2. Design the tool surface

tool name:    stable, verb-noun, describes the workflow step
input schema: typed, narrow, required fields only + optional detail flags
output shape: consistent structure across all tools in the server
failure modes: named error codes + correction hint

3. Design for context limits

  • Default response fits in ~500 tokens for list operations, ~1500 for detail operations.
  • Add limit, page, or summary parameters for large result sets.
  • Truncate deterministically (e.g., top N by recency) — never truncate randomly.

4. Design corrective errors

Bad error: "Error: 404 Not Found"

Good error: "Resource 'project-123' not found. Use list_projects to see available project IDs."

Every error should tell the agent its next valid action.

5. Implement shared infrastructure first

  • Auth handling and token refresh.
  • Retry logic with exponential backoff and rate-limit awareness.
  • Pagination helpers.
  • Output formatting helpers (consistent truncation, redaction of secrets).

6. Evaluate before shipping

  • Write representative task scenarios (not unit tests for individual tools).
  • Check whether an agent can complete the full workflow using only the exposed tools.
  • Redesign weak tools before adding more tools — more tools is not always better.

Server Readiness Checklist

  • Every tool completes a meaningful workflow step.
  • All inputs are typed and schema-validated.
  • Output size is bounded by default.
  • All error messages include a correction hint.
  • Auth and retry are handled in shared infrastructure.
  • At least one end-to-end task scenario has been tested.
  • No secrets appear in tool outputs or error messages.

Local References

  • reference/mcp_best_practices.md
  • reference/python_mcp_server.md
  • reference/node_mcp_server.md
  • reference/evaluation.md

Use the Python and Node references only for the stack you are actually shipping.

Bundled Scripts

  • scripts/evaluation.py — evaluation scaffolding
  • scripts/connections.py — connection-oriented examples

Use them as optional helpers, not mandatory runtime requirements.

What ships with it: 8 files

120.2 KB alongside SKILL.md, 2 of them executable

Gives 0 of the 12 instructions most mcp tooling skills give in 731 tokens

Counted across 638 of the 750 authors here whose files we hold, read 2026-08-07

  • Create ten complex or independent read-only evaluation questionsin 69 of 638, across 15 files
  • Test servers using MCP Inspectorin 61 of 638, across 19 files
  • Provide actionable error messages with specific next stepsin 54 of 638, across 12 files
  • Prioritize comprehensive API coverage over specific workflows or workflow toolsin 54 of 638, across 12 files
  • Use TypeScript and Streamable HTTP for remote servers or clientsin 54 of 638, across 8 files
  • Define structured output schemas where possiblein 50 of 638, across 8 files
  • Use Zod or Pydantic for input schemasin 47 of 638, across 5 files
  • Fetch MCP specification pages with markdown suffixin 46 of 638, across 4 files
  • Load framework documentation using WebFetchin 45 of 638, across 3 files
  • Verify each evaluation answer independentlyin 45 of 638, across 3 files
  • Implement API client with authentication and paginationin 45 of 638, across 3 files
  • Define input schemas with validationin 27 of 638, across 9 files

Said here and by no other author read

  • keep input schemas narrow and typed
  • return size-bounded outputs by default
  • design evaluation cases before shipping
  • merge low-level steps into meaningful operations
  • truncate output deterministically
  • handle auth and retries in shared infrastructure

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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