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io.github.KoyoYeager/pystub

io.github.KoyoYeager/pystub

by KoyoYeager
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

graph

Analyze Python import dependencies from an entry point by building a graph of modules classified as stdlib, third-party, local, builtin, or unresolvable. Returns nodes, edges, and statistics.

Instructions

エントリーポイントからの import グラフを構築して可視化します。

全モジュールの依存関係をノードとエッジで返します。 各ノードは stdlib / third_party / local / builtin / unresolvable に分類されます。

Args: entry_point: プロジェクトのエントリーポイントファイルパス python_path: site-packages パス（空の場合は現在の環境を自動検出） max_depth: 最大探索深度（デフォルト: 5）

Returns: ノード・エッジ・統計情報を含むグラフデータ

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": {
      "entry_point": "<entry_point>"
    },
    "name": "graph"
  }
}

Input Schema

NameRequiredDescriptionDefault
entry_pointYes
python_pathNo
max_depthNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.8/5.0
Behavior3/5

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

The description discloses that it returns nodes classified by module type and includes statistics, which is helpful. However, it does not mention whether the tool is read-only, any side effects, authentication needs, or performance characteristics. With no annotations provided, the description carries the full burden but falls short of full transparency.

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

Conciseness4/5

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

The description is structured with an overview, then Args and Returns sections. It is relatively concise, though the opening line could be more direct. Overall, it is well-organized and not verbose.

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 tool with three parameters and no output schema, the description provides a reasonable overview of what the tool does and returns (nodes, edges, statistics). It could be more detailed about the exact fields in the output, but it is sufficient for an agent to understand the tool's basic functionality.

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?

The input schema provides only titles and types with no descriptions (0% coverage). The description compensates fully by explaining each parameter: entry_point is the project entry point file path, python_path is for site-packages (auto-detected if empty), and max_depth is maximum depth with a default of 5. This adds significant meaning beyond the schema.

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 constructs and visualizes an import graph from an entry point, returning dependency relationships as nodes and edges. It distinguishes itself from sibling tools (analyze, check, generate, generate_submodule) by focusing on dependency graph construction and visualization.

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?

The description does not provide any guidance on when to use this tool versus its siblings (analyze, check, generate, generate_submodule). It lacks explicit usage context, making it harder for an agent to decide which tool to invoke.

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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