en es ja ko zh

mcp-neurolora

Name: mcp-neurolora
Author: aindreyway

by aindreyway

Overview Schema Source Related Servers Discussions Admin

TypeScript

Remote

MCP 脉络藻

MCP 服务器

智能 MCP 服务器，提供使用 OpenAI API 进行代码分析、代码收集和文档生成的工具。

🚀 安装指南

如果您还没有安装任何东西，也不用担心！只需按照以下步骤操作，或者请您的助手协助您安装。

步骤1：安装Node.js

macOS

如果尚未安装 Homebrew，请安装：

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

安装 Node.js 18：

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

视窗

从nodejs.org下载 Node.js 18 LTS
运行安装程序
打开新终端以应用更改

Linux（Ubuntu/Debian）

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

第 2 步：安装 uv 和 uvx

所有操作系统

安装 uv：

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

安装 uvx：
```
uv pip install uvx
```

步骤 3：验证安装

运行以下命令来验证所有内容是否已安装：

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

步骤 4：配置 MCP 服务器

您的助手将帮助您：

找到您的 Cline 设置文件：
- VSCode： ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
- Claude 桌面： ~/Library/Application Support/Claude/claude_desktop_config.json
- Windows VSCode： %APPDATA%/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
- Windows 克劳德： %APPDATA%/Claude/claude_desktop_config.json

添加此配置：

{
  "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"
      }
    }
  }
}

步骤5：安装基础服务器

只需询问您的助手：“请为我的环境安装基础 MCP 服务器”

您的助理将：

找到您的设置文件
运行 install_base_servers 工具
自动配置所有必要的服务器

安装完成后：

完全关闭 VSCode（macOS 上为 Cmd+Q，Windows 上为 Alt+F4）
重新打开 VSCode
新的服务器将可供使用

**重要提示：**安装基础服务器后，需要完全重启 VSCode，以便正确初始化它们。

**注意：**此服务器使用npx直接执行 npm 包，这对于 Node.js/TypeScript MCP 服务器来说是最佳的，可与 npm 生态系统和 TypeScript 工具无缝集成。

Related MCP server: Code Context Provider MCP

基础 MCP 服务器

以下基础服务器将自动安装和配置：

fetch：用于访问 Web 资源的基本 HTTP 请求功能
puppeteer：用于 Web 交互和测试的浏览器自动化功能
顺序思维：用于复杂任务的高级问题解决工具
github：GitHub 集成功能，用于存储库管理
git：Git 操作支持版本控制
shell：基本shell命令执行，常用命令：
- ls：列出目录内容
- cat：显示文件内容
- pwd：打印工作目录
- grep：搜索文本模式
- wc：统计单词、行、字符
- touch：创建空文件
- 查找：搜索文件

🎯 你的助手能做什么

请你的助手：

“分析我的代码并提出改进建议”
“为我的环境安装基础 MCP 服务器”
“从我的项目目录中收集代码”
“为我的代码库创建文档”
“使用我的所有代码生成一个 markdown 文件”

🛠 可用工具

分析代码

使用 OpenAI API 分析代码并生成包含改进建议的详细反馈。

参数：

codePath （必需）：要分析的代码文件或目录的路径

使用示例：

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

该工具将：

使用 OpenAI API 分析您的代码
通过以下方式生成详细反馈：
- 问题和建议
- 违反最佳实践
- 影响分析
- 修复步骤
在您的项目中创建两个输出文件：
- LAST_RESPONSE_OPENAI.txt - 人类可读的分析
- LAST_RESPONSE_OPENAI_GITHUB_FORMAT.json - GitHub 问题的结构化数据

注意：环境配置中需要 OpenAI API 密钥

收集代码

将目录中的所有代码收集到具有语法高亮和导航的单个 markdown 文件中。

参数：

directory （必需）：收集代码的目录路径
outputPath （可选）：保存输出 markdown 文件的路径
ignorePatterns （可选）：要忽略的模式数组（类似于.gitignore）

使用示例：

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

安装基础服务器

将基本 MCP 服务器安装到您的配置文件。

参数：

configPath （必需）：MCP 设置配置文件的路径

使用示例：

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

🔧 功能

服务器提供：

代码分析：
- OpenAI API 集成
- 结构化反馈
- 最佳实践建议
- GitHub 问题生成
代码集合：
- 目录遍历
- 语法高亮
- 导航生成
- 基于模式的过滤
基础服务器管理：
- 自动安装
- 配置处理
- 版本管理

📄 许可证

MIT 许可证 - 请随意在您的项目中使用它！

👤 作者

艾恩德雷韦

GitHub： @aindreyway

⭐️ 支持

如果这个项目对您有帮助，请给⭐️！

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 脉络藻

🚀 安装指南

步骤1：安装Node.js

macOS

视窗

Linux（Ubuntu/Debian）

第 2 步：安装 uv 和 uvx

所有操作系统

步骤 3：验证安装

步骤 4：配置 MCP 服务器

步骤5：安装基础服务器

基础 MCP 服务器

🎯 你的助手能做什么

🛠 可用工具

分析代码

收集代码

安装基础服务器

🔧 功能

📄 许可证

👤 作者

⭐️ 支持

Available Tools

Tool Schema Changelog

Resources

Related MCP Connectors

Related MCP Servers

Docforkofficial

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