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MCP Server Neurolorap

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Python
Hybrid

MCP 서버 Neurolorap

라이센스: MIT 테스트 코드코브

코드 분석 및 문서화를 위한 도구를 제공하는 MCP 서버입니다.

특징

코드 수집 도구

  • 전체 프로젝트에서 코드 수집

  • 특정 디렉토리나 파일에서 코드 수집

  • 여러 경로에서 코드 수집

  • 구문 강조 표시가 있는 마크다운 출력

  • 목차 생성

  • 다양한 프로그래밍 언어 지원

프로젝트 구조 보고 도구

  • 프로젝트 구조 및 지표 분석

  • 마크다운 형식으로 자세한 보고서 생성

  • 파일 크기 및 복잡성 분석

  • 트리 기반 시각화

  • 코드 구성에 대한 권장 사항

  • 사용자 정의 가능한 무시 패턴

Related MCP server: Code Snippet Server

빠른 개요

지엑스피1

종속성을 수동으로 설치하거나 구성할 필요가 없습니다. 이 도구는 코드 분석 및 문서화에 필요한 모든 것을 자동으로 설정합니다.

설치

컴퓨터에 UV >= 0.4.10이 설치되어 있어야 합니다.

서버를 설치하고 실행하려면:

# Install using uvx (recommended)
uvx mcp-server-neurolorap

# Or install using pip (not recommended)
pip install mcp-server-neurolorap

이렇게 하면 자동으로 다음이 수행됩니다.

  • 필요한 모든 종속성을 설치하세요

  • Cline 통합 구성

  • 즉시 사용할 수 있도록 서버를 설정하세요

이 서버는 Cline의 MCP 프로토콜을 통해 제공됩니다. 이를 사용하여 모든 프로젝트의 코드를 분석하고 문서화할 수 있습니다.

용법

개발자 모드

서버에는 직접적인 상호작용을 위한 JSON-RPC 터미널 인터페이스가 있는 개발자 모드가 포함되어 있습니다.

# Start the server in developer mode
python -m mcp_server_neurolorap --dev

사용 가능한 명령:

  • help : 사용 가능한 명령어를 표시합니다

  • list_tools : 사용 가능한 MCP 도구 목록

  • collect <path> : 지정된 경로에서 코드를 수집합니다.

  • report [path] : 프로젝트 구조 보고서 생성

  • exit : 개발자 모드 종료

예제 세션:

> help
Available commands:
- help: Show this help message
- list_tools: List available MCP tools
- collect <path>: Collect code from specified path
- report [path]: Generate project structure report
- exit: Exit the terminal

> list_tools
["code_collector", "project_structure_reporter"]

> collect src
Code collection complete!
Output file: code_collection.md

> report
Project structure report generated: PROJECT_STRUCTURE_REPORT.md

> exit
Goodbye!

MCP 도구를 통해

코드 수집

from modelcontextprotocol import use_mcp_tool

# Collect code from entire project
result = use_mcp_tool(
    "code_collector",
    {
        "input": ".",
        "title": "My Project"
    }
)

# Collect code from specific directory
result = use_mcp_tool(
    "code_collector",
    {
        "input": "./src",
        "title": "Source Code"
    }
)

# Collect code from multiple paths
result = use_mcp_tool(
    "code_collector",
    {
        "input": ["./src", "./tests"],
        "title": "Project Files"
    }
)

프로젝트 구조 분석

# Generate project structure report
result = use_mcp_tool(
    "project_structure_reporter",
    {
        "output_filename": "PROJECT_STRUCTURE_REPORT.md"
    }
)

# Analyze specific directory with custom ignore patterns
result = use_mcp_tool(
    "project_structure_reporter",
    {
        "output_filename": "src_structure.md",
        "ignore_patterns": ["*.pyc", "__pycache__"]
    }
)

파일 저장소

서버는 파일 저장에 구조화된 접근 방식을 사용합니다.

  1. 생성된 모든 파일은 ~/.mcp-docs/<project-name>/ 에 저장됩니다.

  2. 프로젝트 루트에 이 디렉토리를 가리키는 .neurolora 심볼릭 링크가 생성됩니다.

이를 통해 다음이 보장됩니다.

  • 깨끗한 프로젝트 구조

  • 일관된 파일 구성

  • 생성된 파일에 쉽게 접근 가능

  • 다양한 프로젝트 지원

  • 다양한 OS 환경에서 안정적인 파일 동기화

  • IDE 및 파일 탐색기에서 빠른 파일 가시성

무시 패턴 사용자 정의

프로젝트 루트에 .neuroloraignore 파일을 만들어 무시할 파일을 사용자 지정하세요.

# Dependencies
node_modules/
venv/

# Build
dist/
build/

# Cache
__pycache__/
*.pyc

# IDE
.vscode/
.idea/

# Generated files
.neurolora/

.neuroloraignore 파일이 없으면 일반적인 무시 패턴을 사용하여 기본 파일이 생성됩니다.

개발

  1. 저장소를 복제합니다

  2. 가상 환경을 만들고 활성화하세요.

python -m venv .venv
source .venv/bin/activate  # On Unix
# or
.venv\Scripts\activate  # On Windows

  1. 개발 종속성 설치:

pip install -e ".[dev]"

  1. 서버를 실행합니다:

# Normal mode (MCP server with stdio transport)
python -m mcp_server_neurolorap

# Developer mode (JSON-RPC terminal interface)
python -m mcp_server_neurolorap --dev

테스트

이 프로젝트는 자동화된 테스트와 지속적인 통합을 통해 높은 품질 표준을 유지합니다.

  • 80% 이상의 코드 커버리지를 갖춘 포괄적인 테스트 모음

  • Python 3.10, 3.11 및 3.12에 대한 자동화 테스트

  • GitHub Actions를 통한 지속적인 통합

  • 정기적인 보안 검사 및 종속성 검사

개발 및 테스트에 대한 자세한 내용은 PROJECT_SUMMARY.md를 참조하세요.

코드 품질

이 프로젝트는 다양한 도구를 통해 높은 코드 품질 표준을 유지합니다.

# Format code
black .

# Sort imports
isort .

# Lint code
flake8 .

# Type check
mypy src tests

# Security check
bandit -r src/
safety check

이러한 모든 검사는 GitHub Actions를 통한 풀 리퀘스트에서 자동으로 실행됩니다.

CI/CD 파이프라인

이 프로젝트에서는 지속적인 통합 및 배포를 위해 GitHub Actions를 사용합니다.

  • Python 3.10, 3.11 및 3.12에서 테스트를 실행합니다.

  • 코드 형식 및 스타일을 확인합니다.

  • 유형 검사를 수행합니다

  • 보안 검사를 실행합니다

  • 적용 범위 보고서를 생성합니다

  • 패키지를 빌드하고 검증합니다.

  • 테스트 아티팩트 업로드

변경 사항을 병합하려면 파이프라인을 통과해야 합니다.

기여하다

기여를 환영합니다! 자세한 내용은 CONTRIBUTING.md를 참조하세요.

특허

MIT 라이선스. 자세한 내용은 라이선스 파일을 참조하세요.

Available Tools

2 tools
code_collectorC

Collect code from files into a markdown document

ParametersJSON Schema
NameRequiredDescriptionDefault
input_pathNo.
titleNoCode Collection
subproject_idNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.8/5.0
Behavior2/5

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 but only states the basic action. It does not cover critical aspects like whether this is a read-only operation, if it modifies files, error handling, performance implications, or output details. The description is insufficient for a tool with 3 parameters and an output schema.

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 a single, efficient sentence with zero wasted words. It is front-loaded with the core purpose, making it easy to parse quickly. Every word earns its place, though this conciseness comes at the cost of completeness.

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?

Given the tool has 3 parameters with 0% schema coverage, an output schema, and no annotations, the description is inadequate. It does not explain parameter roles, behavioral traits, or how the output schema relates to the markdown document. The presence of an output schema reduces the need to describe return values, but other gaps remain significant.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate but adds no parameter information. It does not explain what 'input_path', 'title', or 'subproject_id' mean, their formats, or how they affect the collection process. The description fails to provide any semantic context beyond the tool's name.

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 the tool's purpose with a specific verb ('collect') and resource ('code from files'), specifying the output format ('into a markdown document'). It distinguishes from the sibling 'project_structure_reporter' by focusing on code content rather than structure, though the distinction could be more explicit.

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 provides no guidance on when to use this tool versus alternatives, such as the sibling 'project_structure_reporter'. It lacks context about appropriate scenarios, prerequisites, or exclusions, leaving the agent to infer usage based solely on the purpose statement.

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

project_structure_reporterC

Generate a report of project structure metrics

ParametersJSON Schema
NameRequiredDescriptionDefault
output_filenameNoPROJECT_STRUCTURE_REPORT.md
ignore_patternsNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.9/5.0
Behavior2/5

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. While 'Generate a report' implies a read-only operation that creates output, it doesn't specify whether this tool scans files, requires specific permissions, has performance implications for large projects, or what format the report takes. The description lacks important behavioral context for a tool that presumably analyzes project structure.

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 a single, efficient sentence that gets straight to the point with no wasted words. It's appropriately sized for what it communicates, though what it communicates is minimal. The structure is clear and front-loaded with the core purpose.

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

Completeness3/5

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

Given that there's an output schema (which should document the return format), the description doesn't need to explain return values. However, for a tool that analyzes project structure with 2 parameters and no annotations, the description is too minimal. It doesn't provide enough context about what 'project structure metrics' includes, how the tool works, or what the parameters control.

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

Parameters2/5

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

With 0% schema description coverage for both parameters, the description provides no information about what 'output_filename' or 'ignore_patterns' mean or how they should be used. The description doesn't mention parameters at all, leaving the agent to guess their purpose from parameter names alone. This is inadequate for a tool with 2 parameters that have no schema documentation.

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 'Generate a report of project structure metrics' clearly states the verb ('Generate') and resource ('report of project structure metrics'), making the purpose understandable. However, it doesn't distinguish this tool from its sibling 'code_collector' - both could potentially involve project analysis, so the distinction isn't explicit.

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 provides no guidance on when to use this tool versus alternatives. There's no mention of when this tool is appropriate, what prerequisites might be needed, or how it differs from the sibling 'code_collector' tool. The agent must infer usage context entirely from the tool name and description.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updatesv1.0.0
    • First observedcode_collector
    • First observedproject_structure_reporter
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.1/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: code_collector focuses on extracting code content into a markdown document, while project_structure_reporter generates metrics about the project's structure. There is no overlap in functionality, making it easy for an agent to choose the right tool.

Naming Consistency5/5

Both tools follow a consistent noun_verb pattern (code_collector and project_structure_reporter), using snake_case throughout. The naming is predictable and readable, with no deviations in style or convention.

Tool Count2/5

With only 2 tools, the server feels thin for a domain like project analysis or code management. This minimal set may not cover essential operations such as code analysis, dependency checking, or file manipulation, limiting its utility for broader tasks.

Completeness2/5

Inferred domain is project/code analysis, but the tool surface is severely incomplete. It lacks basic CRUD operations (e.g., no tools for creating, updating, or deleting files), code quality checks, or integration with version control, leaving significant gaps that could cause agent failures.

Resources

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