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ctx-gen-mcp

ctx-gen-mcp

by liubinmaster
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

scan_skeleton

Scans a code repository to produce a deterministic module skeleton, including root path, module count, file count, and line counts.

Instructions

Scan a code repository and return a deterministic module skeleton.

Args: project_dir: Absolute path to the project root directory. depth: Directory depth for auto-detecting modules (default 2). code_only: If True, only include code files (default True).

Returns: A dict with: root_path, total_modules, total_files, total_lines, modules[].

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": {
      "project_dir": "<project_dir>"
    },
    "name": "scan_skeleton"
  }
}

Input Schema

NameRequiredDescriptionDefault
project_dirYes
depthNo
code_onlyNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior4/5

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

The description clearly outlines deterministic behavior and return structure, but omits potential edge cases or non-obvious side effects. With no annotations, this is adequate but not exhaustive.

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 concise, well-structured with Args and Returns sections, and contains no redundant information.

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?

Covers the return value and parameter semantics, but lacks details on error handling or performance considerations. Sibling context not addressed.

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

Parameters5/5

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

Each parameter is explained with type and default values, adding meaning beyond the schema (e.g., project_dir as absolute path, depth for auto-detection depth).

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 clearly states the tool scans a code repository and returns a deterministic module skeleton, differentiating it from siblings like assemble_docs and validate_coverage.

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 on when to use this tool vs alternatives; the description lacks context for selecting this tool over siblings.

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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