mcpField
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mcp-builder

mcp-builder

github.com/dkyazzentwatwaDevelopment126190 installsUpdated 30w ago
Repository

About mcp-builder

A skill for designing and implementing MCP servers around end-to-end user workflows. It guides developers from API research and tool design through shared infrastructure and realistic evaluation.

Key features

  1. 01Workflow-oriented tool design
  2. 02Tool schemas and docstrings
  3. 03Authentication and request helpers
  4. 04Pagination and response formatting
  5. 05Server evaluation resources

Use cases

  1. 01Plan MCP tools from API documentation
  2. 02Build a Python or TypeScript MCP server
  3. 03Improve error messages and tool outputs
  4. 04Evaluate server usability with realistic tasks

MCP support

Guides developers in creating MCP servers, including tool inventories, input and output schemas, and evaluation prompts or scripts.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill mcp-builder

After installing, type/mcp-builderin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

LICENSE.txt
agents/
openai.yaml
references/
evaluation.md
mcp_best_practices.md
node_mcp_server.md
python_mcp_server.md
scripts/
connections.py
evaluation.py
example_evaluation.xml
requirements.txt

Details

From the project README

MCP Builder

Build MCP servers around user workflows, not raw API endpoints.

Workflow

  1. Read the target API docs and identify the workflows an agent must complete end to end.
  2. Design a small tool surface with high-signal outputs, clear identifiers, and actionable errors.
  3. Implement shared infrastructure first: auth, request helpers, pagination, truncation, and formatting.
  4. Add tool schemas and docstrings that make correct usage obvious.
  5. Evaluate the server with realistic tasks before expanding scope.

Principles

  • Prefer workflow tools over thin endpoint wrappers.
  • Return concise, high-signal responses by default.
  • Use human-readable identifiers whenever possible.
  • Make error messages corrective: tell the agent what to try next.
  • Design for limited context and large datasets.

Resources

  • references/mcp_best_practices.md for design principles that apply to every server.
  • references/python_mcp_server.md for Python implementation patterns.
  • references/node_mcp_server.md for TypeScript implementation patterns.
  • scripts/connections.py and scripts/evaluation.py as repo-local helpers.

Deliverables

  • A concrete tool inventory tied to user workflows.
  • Strict input/output schemas.
  • Evaluation prompts or scripts that confirm the server is usable by an agent.

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