en es ja ko zh

mcp-neurolora

Name: mcp-neurolora
Author: aindreyway

by aindreyway

Overview Schema Source Related Servers Discussions Admin

TypeScript

Remote

MCP 신경병증

MCP 서버

OpenAI API를 사용하여 코드 분석, 코드 수집, 문서 생성을 위한 도구를 제공하는 지능형 MCP 서버입니다.

🚀 설치 가이드

아직 아무것도 설치하지 않았더라도 걱정하지 마세요! 다음 단계를 따르거나 설치 도우미에게 도움을 요청하세요.

1단계: Node.js 설치

맥OS

Homebrew가 설치되어 있지 않으면 설치하세요.
지엑스피1

Node.js 18 설치:

brew install node@18
echo 'export PATH="/opt/homebrew/opt/node@18/bin:$PATH"' >> ~/.zshrc
source ~/.zshrc

윈도우

nodejs.org 에서 Node.js 18 LTS를 다운로드하세요
설치 프로그램을 실행하세요
변경 사항을 적용하려면 새 터미널을 엽니다.

리눅스(우분투/데비안)

curl -fsSL https://deb.nodesource.com/setup_18.x | sudo -E bash -
sudo apt-get install -y nodejs

2단계: uv 및 uvx 설치

모든 운영 체제

uv 설치:

curl -LsSf https://astral.sh/uv/install.sh | sh

uvx 설치:
```
uv pip install uvx
```

3단계: 설치 확인

다음 명령을 실행하여 모든 것이 설치되었는지 확인하세요.

node --version  # Should show v18.x.x
npm --version   # Should show 9.x.x or higher
uv --version    # Should show uv installed
uvx --version   # Should show uvx installed

4단계: MCP 서버 구성

귀하의 조수가 귀하에게 도움을 드릴 것입니다:

Cline 설정 파일을 찾으세요.
- VSCode: ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
- Claude 데스크톱: ~/Library/Application Support/Claude/claude_desktop_config.json
- Windows VSCode: %APPDATA%/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
- Windows Claude: %APPDATA%/Claude/claude_desktop_config.json

다음 구성을 추가합니다.

{
  "mcpServers": {
    "aindreyway-mcp-neurolora": {
      "command": "npx",
      "args": ["-y", "@aindreyway/mcp-neurolora@latest"],
      "env": {
        "NODE_OPTIONS": "--max-old-space-size=256",
        "OPENAI_API_KEY": "your_api_key_here"
      }
    }
  }
}

5단계: 기본 서버 설치

간단히 보조자에게 "내 환경에 맞는 기본 MCP 서버를 설치해 주세요"라고 요청하세요.

귀하의 비서가 수행하는 작업:

설정 파일을 찾으세요
install_base_servers 도구를 실행하세요
필요한 모든 서버를 자동으로 구성합니다

설치가 완료된 후:

VSCode를 완전히 닫습니다(macOS에서는 Cmd+Q, Windows에서는 Alt+F4).
VSCode 다시 열기
새로운 서버를 사용할 준비가 되었습니다.

중요: 기본 서버를 설치한 후에는 VSCode를 완전히 다시 시작해야 제대로 초기화됩니다.

참고: 이 서버는 npm 패키지를 직접 실행하기 위해 npx 사용하는데, 이는 Node.js/TypeScript MCP 서버에 최적화되어 있어 npm 생태계와 TypeScript 툴링과의 원활한 통합을 제공합니다.

Related MCP server: Code Context Provider MCP

기본 MCP 서버

다음 기본 서버는 자동으로 설치 및 구성됩니다.

fetch: 웹 리소스에 접근하기 위한 기본 HTTP 요청 기능
퍼펫티어: 웹 상호작용 및 테스트를 위한 브라우저 자동화 기능
순차적 사고: 복잡한 작업을 위한 고급 문제 해결 도구
github: 저장소 관리를 위한 GitHub 통합 기능
git: 버전 제어를 위한 Git 작업 지원
shell: 일반적인 명령어를 사용한 기본 shell 명령어 실행:
- ls: 디렉토리 내용 나열
- cat: 파일 내용 표시
- pwd: 작업 디렉토리 인쇄
- grep: 텍스트 패턴 검색
- wc: 단어, 줄, 문자 수 세기
- touch: 빈 파일 생성
- find: 파일 검색

🎯 비서가 할 수 있는 일

보조자에게 다음을 요청하세요.

"내 코드를 분석하고 개선 사항을 제안해 주세요"
"내 환경에 맞는 기본 MCP 서버 설치"
"내 프로젝트 디렉토리에서 코드 수집"
"내 코드베이스에 대한 문서를 작성하세요"
"내 모든 코드를 포함하는 마크다운 파일을 생성하세요"

🛠 사용 가능한 도구

분석_코드

OpenAI API를 사용하여 코드를 분석하고 개선 제안과 함께 자세한 피드백을 생성합니다.

매개변수:

codePath (필수): 분석할 코드 파일 또는 디렉토리의 경로

사용 예:

{
  "codePath": "/path/to/your/code.ts"
}

이 도구는 다음을 수행합니다.

OpenAI API를 사용하여 코드 분석
다음을 통해 자세한 피드백을 생성하세요.
- 이슈 및 권장 사항
- 모범 사례 위반
- 영향 분석
- 수정 단계
프로젝트에 두 개의 출력 파일을 만듭니다.
- LAST_RESPONSE_OPENAI.txt - 사람이 읽을 수 있는 분석
- LAST_RESPONSE_OPENAI_GITHUB_FORMAT.json - GitHub 문제에 대한 구조화된 데이터

참고: 환경 구성에 OpenAI API 키가 필요합니다.

수집 코드

구문 강조 및 탐색 기능을 갖춘 디렉토리의 모든 코드를 단일 마크다운 파일로 수집합니다.

매개변수:

directory (필수): 코드를 수집할 디렉토리 경로
outputPath (선택 사항): 출력 마크다운 파일을 저장할 경로
ignorePatterns (선택 사항): 무시할 패턴 배열(.gitignore와 유사)

사용 예:

{
  "directory": "/path/to/project/src",
  "outputPath": "/path/to/project/src/FULL_CODE_SRC_2024-12-20.md",
  "ignorePatterns": ["*.log", "temp/", "__pycache__", "*.pyc", ".git"]
}

설치_베이스_서버

구성 파일에 기본 MCP 서버를 설치합니다.

매개변수:

configPath (필수): MCP 설정 구성 파일에 대한 경로

사용 예:

{
  "configPath": "/path/to/cline_mcp_settings.json"
}

🔧 특징

서버는 다음을 제공합니다.

코드 분석:
- OpenAI API 통합
- 구조화된 피드백
- 모범 사례 권장 사항
- GitHub 이슈 생성
코드 수집:
- 디렉토리 탐색
- 구문 강조 표시
- 탐색 생성
- 패턴 기반 필터링
기본 서버 관리:
- 자동 설치
- 구성 처리
- 버전 관리

📄 라이센스

MIT 라이센스 - 여러분의 프로젝트에서 자유롭게 사용하세요!

👤 저자

에인드리웨이

GitHub: @aindreyway

⭐️ 지원

이 프로젝트가 도움이 되었다면 ⭐️를 눌러주세요!

Available Tools

4 tools

analyze_codeC

Analyze code using OpenAI API (requires your API key). The analysis may take a few minutes. So, wait please.

ParametersJSON Schema

Name	Required	Description	Default
`codePath`	Yes	Absolute path to the code file to analyze (e.g. /Users/username/project/src/code.ts)

TDQS

C2.9/5.0

Behavior3/5

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

With no annotations, the description carries full burden. It discloses that analysis 'may take a few minutes' (timing behavior) and requires an API key (authentication need), which are useful behavioral traits. However, it lacks details on rate limits, error handling, or what happens during analysis (e.g., data sent to OpenAI). No contradiction with annotations exists.

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 concise with three short sentences, front-loading the main action ('Analyze code using OpenAI API'). However, the phrase 'So, wait please' is slightly informal and could be more structured, though it efficiently conveys timing without unnecessary details.

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 no annotations, no output schema, and a simple input schema, the description provides basic context (purpose, timing, auth) but is incomplete. It doesn't explain what the analysis returns (e.g., insights, reports) or potential limitations, leaving gaps for a tool that interacts with an external API and involves waiting.

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?

Schema description coverage is 100%, with the parameter 'codePath' well-documented in the schema as an absolute path. The description adds no additional meaning beyond this, such as file format constraints or analysis scope based on path. Baseline 3 is appropriate as the schema handles parameter documentation adequately.

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

Purpose3/5

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

The description states the tool 'Analyze code using OpenAI API' which provides a verb ('analyze') and resource ('code'), but it's vague about what analysis entails (e.g., security, performance, style). It doesn't distinguish from siblings like 'collect_code' or 'create_github_issues', leaving ambiguity in purpose.

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 mentions 'requires your API key' and 'wait please', implying prerequisites and timing, but offers no explicit guidance on when to use this tool versus alternatives like 'collect_code' for gathering code or 'create_github_issues' for issue tracking. No exclusions or clear context for tool selection are provided.

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

collect_codeC

Collect all code from a directory into a single markdown file

ParametersJSON Schema

Name	Required	Description
`ignorePatterns`	No	Patterns to ignore (similar to .gitignore)
`input`	Yes
`outputPath`	Yes	Path where to save the output markdown file

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. It mentions the action ('collect') and output format ('markdown file'), but fails to detail critical behaviors like whether it overwrites existing files, handles errors (e.g., missing directories), requires specific permissions, or includes metadata in the output. This leaves significant gaps for a tool that modifies files.

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 front-loads the core purpose without unnecessary details. It uses clear language ('collect all code', 'single markdown file') and avoids redundancy, making it easy to parse quickly.

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's complexity (file system operations, output generation) and lack of annotations or output schema, the description is incomplete. It doesn't cover behavioral aspects like error handling, file overwriting, or output structure, which are crucial for safe and effective use. This inadequacy is notable for a tool that creates files.

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 adds minimal parameter semantics beyond the schema, which has 67% coverage. It implies 'input' is for source paths and 'outputPath' for the markdown file, but doesn't explain the dual nature of 'input' (directory vs. list) or how 'ignorePatterns' functions in practice. With moderate schema coverage, the baseline is 3, as the description doesn't fully compensate for the gaps.

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 action ('collect all code') and the output ('into a single markdown file'), specifying both verb and resource. However, it doesn't explicitly differentiate from sibling tools like 'analyze_code' or 'create_github_issues', which might involve code handling but serve different purposes.

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 like 'analyze_code' for code analysis or 'create_github_issues' for issue tracking. It lacks context about prerequisites, such as needing access to the directory, or exclusions, like not being suitable for real-time code processing.

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

create_github_issuesC

Create GitHub issues from analysis results. Requires GitHub token.

ParametersJSON Schema

Name	Required	Description
`issueNumbers`	No	Issue numbers to create (optional, creates all issues if not specified)
`owner`	Yes	GitHub repository owner
`repo`	Yes	GitHub repository name

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the requirement for a GitHub token, which adds some context about authentication needs. However, it fails to describe key behavioral traits such as whether this is a write operation (implied by 'create' but not explicit), potential side effects, error handling, rate limits, or what the output looks like. For a mutation tool with zero annotation coverage, this is a significant gap.

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 concise with two short sentences that are front-loaded with the main purpose. There's no unnecessary verbosity, and each sentence serves a purpose: the first states the action, and the second adds a critical requirement. However, it could be slightly more structured by explicitly separating purpose from prerequisites.

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's complexity as a write operation with no annotations and no output schema, the description is incomplete. It lacks details on behavioral aspects like mutation effects, error cases, and output format. While it mentions a token requirement, it doesn't cover other contextual needs such as permissions or integration with sibling tools, leaving gaps for the agent to infer.

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?

Schema description coverage is 100%, meaning the input schema already documents all parameters thoroughly. The description adds no additional meaning about parameters beyond what's in the schema, such as clarifying the relationship between 'issueNumbers' and 'analysis results' or providing examples. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.

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 action ('create GitHub issues') and the source ('from analysis results'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'analyze_code' or 'collect_code' which might also relate to code analysis workflows, leaving room for ambiguity about when to use this versus other tools in the server.

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 mentions 'Requires GitHub token' which is a prerequisite but not a usage guideline. It provides no guidance on when to use this tool versus alternatives like 'analyze_code' or 'collect_code', nor does it specify scenarios where this tool is appropriate or inappropriate. Without such context, the agent lacks direction on tool selection.

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

install_base_serversC

Install base MCP servers to the configuration

ParametersJSON Schema

Name	Required	Description	Default
`configPath`	Yes	Path to the MCP settings configuration file

TDQS

C2.6/5.0

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. It states the tool performs an installation, implying a write/mutation operation, but fails to describe critical behaviors such as whether it overwrites existing configurations, requires specific permissions, or has side effects like restarting services. This leaves significant gaps in understanding the tool's impact.

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 a single, straightforward sentence that efficiently conveys the core action without unnecessary words. It is appropriately sized for a simple tool, though it could be more front-loaded with additional context to improve clarity.

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?

For a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on what 'base MCP servers' entail, the outcome of the installation, error conditions, or how it interacts with the configuration file. This leaves the agent with insufficient information to use the tool effectively in complex scenarios.

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 input schema has 100% description coverage, clearly documenting the single parameter 'configPath'. The description does not add any meaning beyond what the schema provides, as it mentions no parameters. Given the high schema coverage, the baseline score of 3 is appropriate, as the schema adequately handles parameter semantics without extra help from the description.

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

Purpose3/5

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

The description states the action ('Install') and target ('base MCP servers'), but it's vague about what 'base MCP servers' specifically are and doesn't distinguish this from sibling tools like analyze_code or collect_code. It provides a basic purpose but lacks specificity and differentiation.

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 alternatives or in what context it should be applied. The description does not mention prerequisites, timing, or exclusions, leaving the agent with no usage instructions beyond the basic action.

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.

4 tool updatesv1.0.0Aug 18, 2025
- First observedanalyze_code
- First observedcollect_code
- First observedcreate_github_issues
- First observedinstall_base_servers

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

B3/5.0

Scored 2026-04-18 11:50 across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: analyze_code for code analysis, collect_code for file aggregation, create_github_issues for issue creation, and install_base_servers for server installation. The descriptions clearly differentiate their functions, eliminating any ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., analyze_code, collect_code, create_github_issues, install_base_servers). The naming is uniform and predictable, using snake_case throughout with clear action-object pairs.

Tool Count3/5

With only 4 tools, the set feels thin for a server named 'mcp-neurolora', which suggests a broader scope related to code analysis or AI workflows. While the tools cover specific tasks, the count is borderline low, potentially leaving gaps in functionality for the implied domain.

Completeness2/5

The tool set has significant gaps for a code analysis or AI workflow server. It lacks core operations like retrieving or updating issues, managing analysis results, or handling configurations beyond installation. This incomplete surface will likely cause agent failures in extended workflows.

Resources

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MCP 신경병증

🚀 설치 가이드

1단계: Node.js 설치

맥OS

윈도우

리눅스(우분투/데비안)

2단계: uv 및 uvx 설치

모든 운영 체제

3단계: 설치 확인

4단계: MCP 서버 구성

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