Code Oracle

# Code-Oracle 🔍

### *MCP-Based Repository Diagnostic & Security Engine*

Code-Oracle is a high-performance **Model Context Protocol (MCP)** server built to bridge the gap between AI agents and local codebases. It provides automated architectural mapping, security vulnerability detection, ML asset tracking and quantitative code metrics — all accessible directly from AI assistants like Claude Desktop and Cursor IDE.

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## 🚀 Key Features

* **Architectural Auditing:** Instantly identifies tech stacks (React, Node.js, Flask, Python/ML) and maps core file structures

* **Security SAST:** Static Analysis Security Testing that flags hardcoded passwords, JWT tokens, API keys and exposed .env files

* **ML Asset Discovery:** Specialized tracking for machine learning model weights (.pth, .h5, .onnx) with size reporting

* **Quantitative Metrics:** Calculates total Lines of Code (LOC) broken down by file type while intelligently filtering auto-generated files

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## 🛠️ The "Turbo" Engine — Performance Optimization

To solve the common **"Request Timeout"** issue in the MCP ecosystem, Code-Oracle implements **Surgical Directory Pruning**.

Instead of scanning every file, Code-Oracle uses a **Skip-First logic** that completely bypasses "black-hole" directories like node\_modules, .git and venv at the OS level **before** recursing into them:

dirs\[:] = \[d for d in dirs if d not in \['node\_modules', '.git', 'venv', 'build', 'dist']]

This reduces scan times from ~15 seconds to **under 200ms**, even on massive full stack projects.

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## 📊 Demo Output

| Metric | Value |

| :--- | :--- |

| **Total Lines of Code** | 22,552 |

| **Frontend Logic (JS/JSX)** | 818 LOC |

| **ML Backend (Python)** | 218 LOC |

| **Security Findings** | 4 Critical Leaks Detected |

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## 🔧 4 Built-in Tools

### 1. 🏗️ audit\_project — Architecture Scan

Scans a repository and identifies the tech stack and key source files.

**Example Output:**

{

  "Stack": \["Python/ML", "React/Node.js"],

  "Files": \["flask\_server/app.py", "client/src/App.jsx"]

}

### 2. 🔒 check\_security\_leaks — Security Scanner

Scans Python and JavaScript files for hardcoded secrets and exposed sensitive files.

**Detects:** API keys, passwords, tokens, secrets, MongoDB URIs, exposed .env files

**Example Output:**

{

  "Findings": \[

    "CRITICAL: .env exposed.",

    "WARNING: API\_KEY in config.js"

  ]

}

### 3. 🧠 audit\_ml\_assets — ML Asset Tracker

Discovers all trained ML model files in a repository with their sizes.

**Detects:** .pth, .h5, .onnx, .pb, .weights files

**Example Output:**

{

  "Models Found": \[

    {"model": "model.pth", "size": "9.49 MB"},

    {"model": "model2.pth", "size": "0.05 MB"}

  ]

}

### 4. 📊 get\_code\_stats — Lines of Code Counter

Quantifies the scale of a project by counting lines of code broken down by file type.

**Example Output:**

{

  "Line Count Breakdown": {

    "ML/Python (.py)": 218,

    "Frontend/Logic (.js, .jsx)": 818,

    "Other": 21516

  },

  "Total Custom LOC": 22552

}

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## ⚙️ Installation, Setup & Launch

\# Clone the repository

git clone https://github.com/Sri-Lohith-Mulugu/oracle-server.git



\# Navigate into the project

cd oracle-server



\# Install dependencies

pip install -r requirements.txt



\# Set timeout for stable MCP connection (Windows PowerShell)

$env:MCP\_SERVER\_REQUEST\_TIMEOUT=300000



\# Run the MCP server using Inspector

npx @modelcontextprotocol/inspector python server.py

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## 🔌 Connecting to Claude Desktop

Add this to your Claude Desktop config file (claude\_desktop\_config.json):

{

  "mcpServers": {

    "CodeOracle": {

      "command": "python",

      "args": \["path/to/oracle-server/server.py"]

    }

  }

}

Once connected, you can ask Claude:

- *"Scan my project at C:/Users/me/MyProject"*

- *"Check for security leaks in my repository"*

- *"How many lines of code does my project have?"*

- *"Find all ML models in my project folder"*

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## 📁 Project Structure

oracle-server/

│

├── server.py          # Main MCP server with all 4 tools

├── requirements.txt   # Dependencies (fastmcp)

├── output-demo.png    # Sample MCP Inspector output

└── .gitignore

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## 🌍 Real-World Applications

- 🔍 **Code Review Assistant** — let AI scan your repo before code review

- 🔒 **Security Auditing** — catch hardcoded secrets before pushing to GitHub

- 📊 **Project Metrics** — quickly understand the scale of any codebase

- 🧠 **ML Project Management** — track large model files across repositories

- 🏗️ **Tech Stack Discovery** — instantly understand unfamiliar codebases

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## 🧠 What I Learned

- Building MCP (Model Context Protocol) servers using FastMCP

- Connecting custom Python tools directly into AI assistants like Claude Desktop

- Solving timeout issues in recursive file system operations using Surgical Directory Pruning

- Designing structured JSON responses for AI tool consumption

- Security scanning patterns (SAST) for detecting hardcoded secrets in source code

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## ⚠️ Note

This tool scans **local repositories only** — it does not make any network requests or access remote repositories. All scanning is done on your local file system.

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## 📄 License

This project is for educational and personal use.