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blueprint-mcp

blueprint-mcp

by hjlee624
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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extract_dependency_graph

Extract a dependency graph from a project directory as node-edge JSON, with file or folder granularity for reuse and post-processing.

Instructions

의존성 그래프를 노드·엣지 JSON 으로 추출한다(재사용/후처리용).

Args: path: 분석할 프로젝트 디렉터리의 절대 경로. granularity: "file"(기본) 또는 "folder".

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": {
      "path": "<path>"
    },
    "name": "extract_dependency_graph"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes
granularityNofile

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations provided, the description bears the full burden. It indicates the output format (node-edge JSON) and intended use, but does not disclose side effects, error behavior, or whether the operation is read-only. This is a basic extraction tool, so the lack of detail is moderate.

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, using two sentences to convey the tool's function and parameter meanings. It avoids unnecessary jargon and is well-structured for quick understanding.

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?

The description provides essential context: the output is node-edge JSON for reuse, and parameter roles are clear. It lacks an explicit output schema, but for a simple extraction tool, this level of detail is sufficient. Minor gap is the absence of error handling notes or examples, but not critical.

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?

Although the schema properties lack individual descriptions, the main description explains both parameters: 'path' is an absolute directory path, and 'granularity' has a default of 'file' with an option for 'folder'. This covers 100% of the parameters effectively.

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 extracts a dependency graph and outputs it as node-edge JSON, which is a specific verb-resource combination. It distinguishes from sibling tools like analyze_structure and generate_dependency_diagram by focusing on extraction in JSON format.

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

Usage Guidelines3/5

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

The description mentions the purpose is for reuse/post-processing, but does not explicitly state when to use this tool versus alternatives. It lacks explicit 'use when...' or 'not for...' guidance, leaving the selection somewhat inferred from the purpose.

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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