What can you do with this server?

The MCP Neurolora server is an intelligent tool offering several code-related functionalities: * Analyze Code: Uses OpenAI API to

Which integrations are available for this server?

Provides support for Git operations and version control functionality. Enables repository management, issue creation from code analysis results, and GitHub integration features. Provides seamless integration with the Node.js ecosystem for JavaScript/TypeScript execution and package management. Integrates with OpenAI API for code analysis, providing detailed feedback, improvement suggestions, and best practices recommendations. Offers browser automation capabilities for web interaction and testing. Enables execution of common shell commands like ls, cat, pwd, grep, wc, touch, and find. Provides tooling support for TypeScript projects with optimal integration for TypeScript MCP servers.

How do I use mcp-neurolora?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-neurolora analyze the code in my src directory and suggest improvements" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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mcp-neurolora

by aindreyway

Overview Schema Related Servers Score Discussions

TypeScript

Remote

MCP Neurolora

MCP Server Version License

An intelligent MCP server that provides tools for code analysis using OpenAI API, code collection, and documentation generation.

🚀 Installation Guide

Don't worry if you don't have anything installed yet! Just follow these steps or ask your assistant to help you with the installation.

Step 1: Install Node.js

macOS

Install Homebrew if not installed:

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

Install Node.js 18:

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

Windows

Download Node.js 18 LTS from nodejs.org
Run the installer
Open a new terminal to apply changes

Linux (Ubuntu/Debian)

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

Step 2: Install uv and uvx

All Operating Systems

Install uv:

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

Install uvx:
```
uv pip install uvx
```

Step 3: Verify Installation

Run these commands to verify everything is installed:

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

Step 4: Configure MCP Server

Your assistant will help you:

Find your Cline settings file:
- VSCode: ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
- Claude Desktop: ~/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

Add this configuration:

{
  "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"
      }
    }
  }
}

Step 5: Install Base Servers

Simply ask your assistant: "Please install the base MCP servers for my environment"

Your assistant will:

Find your settings file
Run the install_base_servers tool
Configure all necessary servers automatically

After the installation is complete:

Close VSCode completely (Cmd+Q on macOS, Alt+F4 on Windows)
Reopen VSCode
The new servers will be ready to use

Important: A complete restart of VSCode is required after installing the base servers for them to be properly initialized.

Note: This server uses npx for direct npm package execution, which is optimal for Node.js/TypeScript MCP servers, providing seamless integration with the npm ecosystem and TypeScript tooling.

Related MCP server: Code Context Provider MCP

Base MCP Servers

The following base servers will be automatically installed and configured:

fetch: Basic HTTP request functionality for accessing web resources
puppeteer: Browser automation capabilities for web interaction and testing
sequential-thinking: Advanced problem-solving tools for complex tasks
github: GitHub integration features for repository management
git: Git operations support for version control
shell: Basic shell command execution with common commands:
- ls: List directory contents
- cat: Display file contents
- pwd: Print working directory
- grep: Search text patterns
- wc: Count words, lines, characters
- touch: Create empty files
- find: Search for files

🎯 What Your Assistant Can Do

Ask your assistant to:

"Analyze my code and suggest improvements"
"Install base MCP servers for my environment"
"Collect code from my project directory"
"Create documentation for my codebase"
"Generate a markdown file with all my code"

🛠 Available Tools

analyze_code

Analyzes code using OpenAI API and generates detailed feedback with improvement suggestions.

Parameters:

codePath (required): Path to the code file or directory to analyze

Example usage:

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

The tool will:

Analyze your code using OpenAI API
Generate detailed feedback with:
- Issues and recommendations
- Best practices violations
- Impact analysis
- Steps to fix
Create two output files in your project:
- LAST_RESPONSE_OPENAI.txt - Human-readable analysis
- LAST_RESPONSE_OPENAI_GITHUB_FORMAT.json - Structured data for GitHub issues

Note: Requires OpenAI API key in environment configuration

collect_code

Collects all code from a directory into a single markdown file with syntax highlighting and navigation.

Parameters:

directory (required): Directory path to collect code from
outputPath (optional): Path where to save the output markdown file
ignorePatterns (optional): Array of patterns to ignore (similar to .gitignore)

Example usage:

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

install_base_servers

Installs base MCP servers to your configuration file.

Parameters:

configPath (required): Path to the MCP settings configuration file

Example usage:

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

🔧 Features

The server provides:

Code Analysis:
- OpenAI API integration
- Structured feedback
- Best practices recommendations
- GitHub issues generation
Code Collection:
- Directory traversal
- Syntax highlighting
- Navigation generation
- Pattern-based filtering
Base Server Management:
- Automatic installation
- Configuration handling
- Version management

📄 License

MIT License - feel free to use this in your projects!

👤 Author

Aindreyway

GitHub: @aindreyway

⭐️ Support

Give a ⭐️ if this project helped you!

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. Dates show when Glama detected each change.

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

Install Server

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TDQS

B3/5.0

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

Need Help?

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Looking for Admin?

If you are the server author, to access and configure the admin panel.

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MCP Neurolora

🚀 Installation Guide

Step 1: Install Node.js

macOS

Windows

Linux (Ubuntu/Debian)

Step 2: Install uv and uvx

All Operating Systems

Step 3: Verify Installation

Step 4: Configure MCP Server

Step 5: Install Base Servers

Base MCP Servers

🎯 What Your Assistant Can Do

🛠 Available Tools

analyze_code

collect_code

install_base_servers

🔧 Features

📄 License

👤 Author

⭐️ Support

Available Tools

Tool Schema Changelog

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Docforkofficial

Appeared in Searches

Latest Blog Posts

MCP directory API