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Engineering Documentation MCP Server

Engineering Documentation MCP Server

by mfaizanmumtaz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

generate_project_documentation

Analyze a repository, generate documentation, publish to Google Docs, and optionally export to Markdown, HTML, or PDF. Returns the Google Docs URL and export paths.

Instructions

Run the full documentation pipeline on a repository: analyze the code, generate all documentation sections, publish to Google Docs, and optionally export to Markdown/HTML/PDF. Returns the Google Docs URL and export paths.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "repo_path": "<repo_path>"
    },
    "name": "generate_project_documentation"
  }
}

Input Schema

NameRequiredDescriptionDefault
titleNoCustom document title. Defaults to "<repo-name> — Engineering Documentation".
repo_pathYesPath to the repository root directory.
export_pdfNoSave a PDF file to the output directory.
share_withNoList of email addresses to share the Google Doc with.
export_htmlNoSave an HTML file to the output directory.
export_markdownNoSave a Markdown file to the output directory.
publish_to_google_docsNoCreate and populate a Google Doc.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose that the tool analyzes code, publishes to Google Docs, exports files, and returns URLs/paths, but it omits side effects, prerequisites (e.g., authentication), and conditional behavior such as interactions between publish_to_google_docs and share_with.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, action-first, with a clear colon-separated list of steps and the return value. Every clause contributes to understanding the tool's role, making it concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with seven parameters and an output schema, the description captures the primary workflow, optional exports, and return values. It could improve by suggesting when to choose individual sibling tools, but the current level is adequate for selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents each parameter. The description only reiterates 'optionally export to Markdown/HTML/PDF' and adds no new meaning beyond what the input schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description immediately states the tool's core action with 'Run the full documentation pipeline on a repository' and enumerates each stage (analyze, generate, publish, optionally export). This clearly distinguishes it from sibling tools like generate_readme or create_google_doc, which handle only parts of the pipeline.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'full documentation pipeline' communicates a comprehensive workflow, and the sibling tool names suggest individual steps, so the intended use case is clear. However, it lacks explicit 'use this instead of X when...' guidance or exclusions, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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