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liubinmaster

ctx-gen-mcp

by liubinmaster

assemble_docs

Assembles per-module JSON context files into progressive-disclosure Markdown documentation, organizing code context for efficient AI agent understanding.

Instructions

Assemble all per-module JSON context files into progressive-disclosure MD docs.

Args: project_dir: Path to the project root. ctx_dir: Path to the ctx/ directory with per-module JSONs. out_docs: Output directory for MD docs. project_name: Optional project name (default: inferred from project_dir).

Returns: Dict with: main_doc, module_docs[], errors[].

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_dirYes
ctx_dirYes
out_docsYes
project_nameNo
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It does not mention file system side effects (reading JSON, writing MD), permissions required, or whether it overwrites existing files. The return value description is minimal.

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

Conciseness3/5

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

The description is relatively short but includes the Args section which largely repeats the input schema. Given no schema descriptions, this is acceptable but could be more concise by integrating parameter details into a single paragraph.

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

Completeness2/5

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

The description mentions output structure (Dict with main_doc, module_docs[], errors[]) which is helpful. However, it lacks details on prerequisites (e.g., JSON files must exist), error conditions, or how this fits with sibling tools. No output schema exists, so more detail on return values would benefit completeness.

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?

The description includes an Args section with brief explanations for each parameter, compensating for 0% schema coverage. However, these are minimal (e.g., 'Path to the project root') and do not add context like formatting constraints or defaults beyond what the schema already indicates.

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

Purpose4/5

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

The description clearly states 'Assemble all per-module JSON context files into progressive-disclosure MD docs', which specifies a concrete verb and resource. However, it does not differentiate from sibling tools like scan_skeleton or validate_coverage, missing an opportunity to clarify its role in 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 Guidelines2/5

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

No guidance is provided on when to use this tool versus its siblings (scan_skeleton, validate_coverage). The description should indicate that this tool is typically used after scanning and validation are complete.

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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