🧠 IFS Cloud MCP Server

AI-powered Model Context Protocol server for intelligent IFS Cloud codebase analysis

A sophisticated Model Context Protocol (MCP) server that provides AI agents with deep understanding of IFS Cloud codebases through comprehensive analysis, PageRank importance ranking, and intelligent code search capabilities.

✨ Key Features

🎯 Intelligent Code Analysis

• Comprehensive File Analysis: Extracts API calls, procedure/function names, and dependency relationships
• PageRank Ranking: Identifies the most important files based on dependency network analysis
• Reference Graph: Maps inter-file dependencies for architectural understanding
• Multi-format Support: Handles .plsql, .entity, .client, .projection, .fragment, and more

📦 Version Management

• ZIP Import: Import complete IFS Cloud releases from ZIP files
• Multiple Versions: Manage and switch between different IFS Cloud versions
• Isolated Analysis: Each version maintains separate analysis data
• Smart Extraction: Automatically filters and organizes supported file types

🔍 Advanced Hybrid Search & Discovery

• Dual-Query Hybrid Search: Separate semantic and lexical queries for precision control
• BGE-M3 Semantic Search: AI-powered understanding using state-of-the-art embeddings
• BM25S Lexical Search: Fast exact matching for API names, functions, and keywords
• FlashRank Fusion: Neural reranking combines semantic and lexical results intelligently
• Three Search Modes: Comprehensive, semantic-only, or lexical-only via MCP tools
• PageRank Importance: Files ranked by their significance in the dependency network
• CUDA Acceleration: GPU-powered semantic search for maximum performance

🚀 Quick Start

1. Installation

git clone https://github.com/graknol/ifs-cloud-core-mcp-server.git
cd ifs-cloud-core-mcp-server
uv sync

2. Import IFS Cloud Version

# Import an IFS Cloud ZIP file
uv run python -m src.ifs_cloud_mcp_server.main import "IFS_Cloud_25.1.0.zip" --version "25.1.0"

3. Analyze the Codebase

# Perform comprehensive analysis
uv run python -m src.ifs_cloud_mcp_server.main analyze --version "25.1.0"

# Calculate PageRank importance scores
uv run python -m src.ifs_cloud_mcp_server.main calculate-pagerank --version "25.1.0"

4. Start the MCP Server

# Start server with analyzed version
uv run python -m src.ifs_cloud_mcp_server.main server --version "25.1.0"

📋 CLI Commands Reference

Version Management

# Import a ZIP file
uv run python -m src.ifs_cloud_mcp_server.main import <zip_file> --version <version_name>

# Download pre-built indexes from GitHub (fastest setup)
uv run python -m src.ifs_cloud_mcp_server.main download --version <version> [--force]

# List all versions
uv run python -m src.ifs_cloud_mcp_server.main list

# Delete a version
uv run python -m src.ifs_cloud_mcp_server.main delete --version <version_name> [--force]

Analysis Commands

# Analyze codebase (extract dependencies, API calls, etc.)
uv run python -m src.ifs_cloud_mcp_server.main analyze --version <version> [--max-files N] [--force]

# Calculate PageRank importance scores
uv run python -m src.ifs_cloud_mcp_server.main calculate-pagerank --version <version>

# Create embeddings for semantic search (uses BGE-M3 model)
uv run python -m src.ifs_cloud_mcp_server.main embed --version <version> [--max-files N]

# Create test embeddings (top 10 files for quick testing)
uv run python -m src.ifs_cloud_mcp_server.main embed --version <version> --max-files 10

Server Operation

# Start MCP server
uv run python -m src.ifs_cloud_mcp_server.main server --version <version>

# Start web UI (if available)
uv run python -m src.ifs_cloud_mcp_server.web_ui

� MCP Search Tools

The server provides three sophistic

... View full README on GitHub