Repository Intelligence

Analyze repos of any size - security scanning code analysis monorepo support

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "repository-intelligence": {
      "command": "npx",
      "args": [
        "-y",
        "repository-intelligence"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

Lyra Intel

Complete Intelligence Infrastructure Engine for Massive-Scale Codebase Analysis

📚 Full Documentation | Quick Start | Use Cases | API Reference

Analyze codebases 10-100x faster with AI-powered insights, security scanning, and semantic search.

⚡ Active Development

Lyra Intel is actively being enhanced with improvements daily. The core platform is production-ready and being used in enterprise deployments. Thank you for your contributions! 🙌

Why Lyra Intel?

Most code analysis tools force a choice: automation at the cost of understanding, or manual inspection with no scale.

Lyra Intel is built on a different principle: Give developers and security teams the intelligence they need to make informed decisions at scale.

You get:

✅ Complete visibility - Understand your entire codebase, not just highlighted issues
✅ AI-powered insights - Get context and explanations, not just lists of problems
✅ Security you control - Run locally or in your cloud, with no data leaving your infrastructure
✅ Scale without sacrifice - Analyze 1 million lines or 1 billion lines with the same ease
✅ Open source - Full transparency, no vendor lock-in, customize for your needs

Perfect for teams that want to own their code intelligence.

What is Lyra Intel?

Lyra Intel is a comprehensive, production-ready intelligence platform designed to understand, secure, and improve codebases of any size - from small projects to enterprise monorepos with millions of lines of code.

Unlike traditional linters or SonarQube-style tools, Lyra Intel combines:

Deep code analysis (AST parsing, dependency graphs, complexity metrics)
AI-powered insights (OpenAI, Anthropic, or local models)
Semantic code search (ML-powered search beyond keywords)
Security scanning (secrets, OWASP, CVE detection)
Knowledge graphs (understand relationships in your code)
Forensic analysis (find dead code, document gaps, technical debt)

Why You Need Lyra Intel

For Security Teams:

Automatically find hardcoded secrets, SQL injection risks, OWASP vulnerabilities
Track security across massive codebases without manual scanning
Generate compliance reports (SOC2, HIPAA, PCI-DSS ready)

For Development Teams:

Understand unfamiliar codebases in hours, not weeks
Find dead code and technical debt before they become problems
Make data-driven architectural decisions
Detect complex bugs that static analysis misses

For Engineering Leaders:

Quantify code quality and technical debt
Track metrics across teams and projects
Plan migrations and upgrades with confidence
Reduce time spent on code reviews

What You Can Do

With 70+ specialized components, Lyra Intel enables:

Goal	What Lyra Intel Does	Time Saved
Secure a legacy codebase	Scan for vulnerabilities, create remediation plan	Weeks → Hours
Onboard new developers	Build searchable knowledge base, find examples	Days → Hours
Plan a framework upgrade	Analyze impact, generate step-by-step migration plan	Months → Days
Understand technical debt	Quantify debt, track trends, prioritize fixes	Ongoing → Automated
Review pull requests	AI-powered insights + security checks + complexity analysis	30 min → 5 min
Find security issues	Scan for 50+ vulnerability patterns in real-time	Manual → Automated

See real-world use cases →

🚀 Features

Lyra Intel includes 70+ specialized components organized by capability:

View All Features (70+ Components)

Core Analysis - Understand Your Code

📁 File Crawler - Parallel directory traversal with streaming for memory efficiency. Process millions of files without memory issues.
📜 Git Collector - Complete commit history, blame analysis, contributor stats. Understand who changed what and when.
🔍 AST Analyzer - Multi-language syntax tree parsing (Python, JS/TS, Go, Rust, Java, C++, C#, Ruby, PHP). Get accurate code structure.
🔗 Dependency Mapper - Build complete dependency graphs with circular detection. Understand your architecture.
⚠️ Pattern Detector - Find code smells, anti-patterns, security issues. Detect problems before they become expensive.

Scalability - From Laptop to Enterprise

🖥️ Local Mode - Single machine analysis for development. No setup needed, runs instantly on your machine.
🌐 Distributed Mode - Multi-worker processing for larger codebases. Scale analysis to 100K+ files efficiently.
☁️ Cloud Massive Mode - Auto-scaling cloud infrastructure (AWS, GCP, Azure). Analyze monorepos with millions of files.

Storage Options - Flexibility for Any Scale

SQLite - Local development and small projects. Built-in, no dependencies.
PostgreSQL - Production deployments. Reliable, proven, scalable.
BigQuery - Massive-scale analytics. Query 1M+ analysis results instantly.
Cache Layer - Memory, File, Redis backends with TTL/LRU eviction. Speed up repeated analyses.

🔐 Security - Find Vulnerabilities Before They Become Breaches

Security Scanner - OWASP Top 10, hardcoded secrets, SQL injection detection. Scan 50+ vulnerability patterns.
Vulnerability Database - Track known CVEs and advisories. Stay updated on emerging threats.
Custom Rules - Define custom security patterns. Enforce your organization's security standards.

🤖 AI Integration - Get Smarter Insights

AI Analyzer - Code explanation, bug detection, refactoring suggestions. Understand complex code instantly.
Multiple Providers - OpenAI (GPT-4/3.5), Anthropic (Claude), or Local (Ollama/llama.cpp). Choose what fits your workflow.
Cost Effective - Local models for free analysis, or cloud models for maximum accuracy.

📊 Visualization & Reports - Communicate Results

Graph Generator - Export to D3.js, Mermaid, Graphviz DOT. Visualize dependencies and architecture.
Report Generator - Executive, Technical, Security, Architecture reports. Different reports for different audiences.
Web Dashboard - Interactive D3.js/Cytoscape visualization. Explore your codebase visually.

🌐 API & Enterprise Features

REST API Server - 15+ endpoints for integration. Build on top of Lyra Intel.
Authentication - API Key, JWT, OAuth 2.0 (SSO), LDAP support. Secure access control.
RBAC - Role-based access control. Manage permissions across your team.
Rate Limiting - Protect your infrastructure. Scale safely.

🔬 Forensic Analysis - Find Hidden Problems

Forensic Analyzer - Code↔doc bidirectional mapping. Find documentation gaps automatically.
Dead Code Detector - Find unused functions, classes, imports. Clean up your codebase.
Complexity Analyzer - Cyclomatic, Cognitive, Halstead metrics. Identify problematic code.

📋 More Capabilities

Code Generation - AI-powered function/class/API generation with custom templates
Diff & Impact Analysis - Understand what changed and why it matters
Migration Planning - Plan framework/version upgrades with step-by-step guidance
Code Profiling - Detect N+1 queries, blocking I/O, inefficient algorithms
Schema Analysis - Database schema analysis from ORM models
Documentation Generator - Auto-generate API docs and changelogs
Workflow Engine - Define and execute multi-step analysis pipelines

🔍 Auto-Discovery Pipeline (NEW)

GitHub Scanner - Automatically discover new MCP crypto tools from GitHub
AI Tool Analyzer - Extract tool definitions using AI/pattern matching
Security Scanner - Scan discovered tools for vulnerabilities
Registry Submitter - Submit approved tools to the Lyra Registry
Daily Automation - GitHub Actions workflow for continuous discovery

See Discovery Documentation →

📚 Complete Documentation

Lyra Intel includes comprehensive documentation covering every aspect of the platform:

Core Documentation

📖 FEATURES.md - Detailed feature documentation with code examples for:
- Semantic Search (ML-powered code search)
- SSO Integration (OAuth 2.0, SAML 2.0, LDAP)
- Language Parsers (C++, C#, Ruby, PHP)
- Plugin System
- IDE Extensions (VS Code, JetBrains)
- CI/CD Integrations (GitLab, Bitbucket, GitHub Actions)
- Export Formats (PDF, SARIF, Excel, CSV)
- WebSocket Streaming
- Interactive CLI
- Web Dashboard
- Monitoring & Metrics (Prometheus, Grafana)
💻 EXAMPLES.md - Working code examples for:
- Quick start (60-second analysis)
- Core analysis workflows
- Semantic search usage
- SSO setup and configuration
- Language-specific parsing
- Custom plugin development
- IDE extension installation
- CI/CD pipeline integration
- Real-time WebSocket streaming
- Monitoring setup
- Complete end-to-end workflows
🏗️ ARCHITECTURE.md - Technical architecture documentation:
- System overview and design
- Core component architecture
- Data flow diagrams
- Module organization
- Extension points
- Deployment architectures (single server, Kubernetes, AWS)
- Performance & scalability
- Security architecture
- Technology stack

Read the full README →View source on GitHub →

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Frequently Asked Questions about Repository Intelligence

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "repository-intelligence": { "command": "npx", "args": ["-y", "Repository Intelligence"] } }

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Technical Specs & Signals

CategoryDeveloper Tools

27Quality signal: Emerging · 27/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20

Documentation & tools11/30

Adoption & activity1/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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