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MCP Server Neurolorap

by aindreyway
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

MCP Server Neurolorap

License: MIT Tests codecov

MCP server providing tools for code analysis and documentation.

Features

Code Collection Tool

  • Collect code from entire project

  • Collect code from specific directories or files

  • Collect code from multiple paths

  • Markdown output with syntax highlighting

  • Table of contents generation

  • Support for multiple programming languages

Project Structure Reporter Tool

  • Analyze project structure and metrics

  • Generate detailed reports in markdown format

  • File size and complexity analysis

  • Tree-based visualization

  • Recommendations for code organization

  • Customizable ignore patterns

Related MCP server: Code Snippet Server

Quick Overview

# Using uvx (recommended)
uvx mcp-server-neurolorap

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

You don't need to install or configure any dependencies manually. The tool will set up everything you need to analyze and document code.

Installation

You'll need to have UV >= 0.4.10 installed on your machine.

To install and run the server:

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

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

This will automatically:

  • Install all required dependencies

  • Configure Cline integration

  • Set up the server for immediate use

The server will be available through the MCP protocol in Cline. You can use it to analyze and document code from any project.

Usage

Developer Mode

The server includes a developer mode with JSON-RPC terminal interface for direct interaction:

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

Available commands:

  • help: Show available commands

  • list_tools: List available MCP tools

  • collect <path>: Collect code from specified path

  • report [path]: Generate project structure report

  • exit: Exit developer mode

Example session:

> 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!

Through MCP Tools

Code Collection

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"
    }
)

Project Structure Analysis

# 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__"]
    }
)

File Storage

The server uses a structured approach to file storage:

  1. All generated files are stored in ~/.mcp-docs/<project-name>/

  2. A .neurolora symlink is created in your project root pointing to this directory

This ensures:

  • Clean project structure

  • Consistent file organization

  • Easy access to generated files

  • Support for multiple projects

  • Reliable file synchronization across different OS environments

  • Fast file visibility in IDEs and file explorers

Customizing Ignore Patterns

Create a .neuroloraignore file in your project root to customize which files are ignored:

# Dependencies
node_modules/
venv/

# Build
dist/
build/

# Cache
__pycache__/
*.pyc

# IDE
.vscode/
.idea/

# Generated files
.neurolora/

If no .neuroloraignore file exists, a default one will be created with common ignore patterns.

Development

  1. Clone the repository

  2. Create and activate virtual environment:

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

  1. Install development dependencies:

pip install -e ".[dev]"

  1. Run the server:

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

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

Testing

The project maintains high quality standards through automated testing and continuous integration:

  • Comprehensive test suite with over 80% code coverage

  • Automated testing on Python 3.10, 3.11, and 3.12

  • Continuous integration through GitHub Actions

  • Regular security scans and dependency checks

For development and testing details, see PROJECT_SUMMARY.md.

Code Quality

The project maintains high code quality standards through various tools:

# Format code
black .

# Sort imports
isort .

# Lint code
flake8 .

# Type check
mypy src tests

# Security check
bandit -r src/
safety check

All these checks are run automatically on pull requests through GitHub Actions.

CI/CD Pipeline

The project uses GitHub Actions for continuous integration and deployment:

  • Runs tests on Python 3.10, 3.11, and 3.12

  • Checks code formatting and style

  • Performs type checking

  • Runs security scans

  • Generates coverage reports

  • Builds and validates package

  • Uploads test artifacts

The pipeline must pass before merging any changes.

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

License

MIT License. See LICENSE file for details.

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
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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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