Booster MCP
Booster MCP turns complex codebases into understandable systems with semantic search, 3D visualization, and debugging tools.
README
🚀 Booster MCP — Semantic Code Intelligence for Large Codebases
🎯 What You Get
Booster MCP turns complex codebases into understandable systems.
You spend less time searching and more time building. Instead of:
- ❌ Manually grepping through thousands of files
- ❌ Getting lost in unfamiliar architectures
- ❌ Debugging by guessing
You get:
- ✅ Instant semantic understanding — ask questions, get answers across your entire codebase
- ✅ Visual architecture maps — see your code as a 3D "Code City"
- ✅ Automatic context injection — include exactly what the AI needs to fix bugs or add features
- ✅ Debugging superpowers — flipchart sessions with call graphs, sequence diagrams, and session notes
- ✅ 7 battle-tested agent skills — pre-built workflows for onboarding, refactoring, bug hunting, and code review
Real-World Impact
| Problem | Old Way | With Booster |
|---|---|---|
| New developer onboarding | 2-3 days of grep | 30 min interactive map + context |
| Finding related code | Manual search | Semantic search + symbol graph |
| Debugging production issues | Stack traces → manual tracing | analyze_error() → call graph → fix |
| Code review bottlenecks | Hours of manual review | Semantic + dependency analysis |
⚡ Quick Start (1 minute)
One-Click Install
macOS / Linux:
curl -fsSL https://raw.githubusercontent.com/NeuroGhostDev/Booster-mcp/main/install.sh | bash
Windows (PowerShell):
Invoke-WebRequest -Uri "https://raw.githubusercontent.com/NeuroGhostDev/Booster-mcp/main/install.ps1" -OutFile "install.ps1" | .\install.ps1
First Command
add_repo("C:\\my-project")
get_code_city() # Opens 3D visualization in browser
That's it. Your codebase is now AI-searchable.
💎 Features at a Glance
🔍 Semantic Search (Not Just Grep)
- Vector-powered code search understands intent, not just keywords
- Find "authentication flow" and get relevant login, OAuth, token validation code
- Works across multiple languages
🗺️ Code City 3D
- Visual representation of your project structure
- Buildings = files (height = complexity, color = language)
- Instantly spot architectural patterns and dependencies
📊 Repository Maps
- One-page project overview for onboarding
- Auto-generated, always in sync
- Includes conventions, stack, and module boundaries
🐛 Flipchart Debugging
- Create debug sessions with call graphs and sequence diagrams
- Mermaid-powered visualizations
- Add notes and track hypotheses across symbols
🤖 7 Built-in Agent Skills
booster-onboard— new codebase? Start herebooster-context-inject— give AI exactly what it needsbooster-bug-hunt— stack trace → diagnosis → fixbooster-feature-add— find patterns, add consistent codebooster-deep-dive— understand architecturebooster-refactor— impact analysis + change automationbooster-review— semantic code review
🔗 Context Injection
repo://map → Repository structure
repo://stack → Dependencies and frameworks
repo://conventions → Code patterns and standards
Auto-sync with live documentation via fetch_stack_docs().
⚙️ Incremental Indexing
- Watchdog monitors changes
- No manual reindex needed
- Multi-repo support without server restart
📦 What's Included
| Component | Purpose |
|---|---|
| FastMCP Server | Core tool definitions and routing |
| Vector Indexer | FAISS-backed semantic search across code |
| Graph Engine | Call graphs, import graphs, dependency analysis |
| Code City Visualizer | 3D HTML visualization of codebase |
| Agent Skills | 7 pre-built workflows for common tasks |
| Web UI | Manage repos, generate visualizations, inspect maps |
| Flipchart | Debug sessions with Mermaid diagrams |
| Toolkit | Grep, git, command execution, error analysis |
🛠️ Installation
Requirements
- Python 3.11+
- Git
- Internet (first run downloads embedding model)
Full Manual Install (all platforms)
Clone & Setup:
git clone https://github.com/NeuroGhostDev/Booster-mcp.git
cd Booster-mcp
python3.11 -m venv .venv
source .venv/bin/activate # or .\.venv\Scripts\Activate.ps1 on Windows
pip install -r requirements.txt
Start:
python server.py
Configure Your MCP Client
Add to your .claude_desktop_config.json (Claude Desktop, Cline, etc):
macOS/Linux:
{
"mcpServers": {
"Booster": {
"command": "/home/user/Booster-mcp/.venv/bin/python",
"args": ["/home/user/Booster-mcp/server.py"]
}
}
}
Windows (use absolute paths):
{
"mcpServers": {
"Booster": {
"command": "C:\\Users\\YourName\\Booster-mcp\\.venv\\Scripts\\python.exe",
"args": ["C:\\Users\\YourName\\Booster-mcp\\server.py"]
}
}
}
🚀 Common Workflows
Onboard a New Project
add_repo("C:\\project")
repo_stats() # Quick metrics
get_repo_map() # Architecture overview
semantic_search("main entry point")
find_symbol("main") # Navigate to key functions
Debug a Production Error
analyze_error("TypeError: 'NoneType' object is not subscriptable")
flipchart_quick_debug("handler_function", max_depth=3)
read_with_context("auth.py", line=42, context=10)
git_diff("auth.py") # See recent changes
Add a Feature Safely
semantic_search("similar feature pattern")
find_symbol("existing_feature")
flipchart_sequence_diagram("existing_feature", depth=5)
# Now safe to implement
Code Review with Superpowers
flipchart_call_graph("modified_function", max_depth=5)
external_deps("modified_function")
find_duplicates(min_lines=5)
📚 Full Documentation
- COOKBOOK.md — Deep-dive recipes and advanced usage
- MARKETPLACE.md — Publish to MCP catalogs
🎓 System Instructions for Maximum Power
Add this to your MCP client for best results:
MCP Usage Policy:
- New codebase? Use onboard skill first
- Production error? Use bug-hunt skill
- Architecture question? Use deep-dive skill
- Adding feature? Use feature-add skill + inject context
- Refactoring? Use refactor skill + analyze impact
- Code review? Use review skill + semantic analysis
Always prefer semantic tools over grep for understanding.
[See full system instructions in README]
🛠️ All Available Tools
Repository Management
add_repo(path)— Index a new repositoryremove_repo(path)— Stop trackingreindex_repo(path)— Force re-indexlist_repos()— See active reposrepo_stats()— Size, symbols, metrics
Search & Navigation
semantic_search(query)— Find code by meaningfind_symbol(name)— Locate functions, classes
Context Injection
inject_context()— Auto-build AI contextfetch_stack_docs()— Live dependency docs
Debugging
flipchart_quick_debug(symbol)— Instant graphsflipchart_call_graph(symbol)— Show callers/calleesflipchart_sequence_diagram(symbol)— Flow diagramanalyze_error(stacktrace)— Error analysis
Utilities
code_grep(pattern)— Smart grepread_with_context(file, line)— Show code + contextgit_log(path)— See historyrun_command(cmd)— Execute toolsfind_duplicates()— Code duplicationexternal_deps(symbol)— See dependencies
Visualization
get_code_city()— 3D visualizationget_repo_map()— Architecture map
🌍 Language Support
- Python
- JavaScript / TypeScript
- Rust
- Go
- Java
- C / C++
🧪 Testing
python test_mcp.py # Basic tests
python test_all.py # Full suite
❓ FAQ
Q: Will this slow down my codebase?
A: No. Indexing is separate. Your code runs normally.
Q: Does it work with private repos?
A: Yes. Everything runs locally. No data leaves your machine.
Q: Which AI clients are supported?
A: Any MCP-compatible client (Claude Desktop, Cline, Continue, etc).
Q: Can I index multiple repos at once?
A: Yes. add_repo() as many as you want. No restart needed.
Q: What if my repo is 1M+ lines?
A: Works fine. Semantic indexing is built for scale. Use .ignore to skip heavy directories.
📖 Detailed Docs & Guides
- COOKBOOK.md — Recipes, examples, advanced techniques
- MARKETPLACE.md — Publish to Smithery, Glama
🤝 Contributing
- Fork it
- Branch:
git checkout -b feature/my-feature - Commit:
git commit -m "Add feature" - Test:
python test_all.py - Push & open PR
📄 License
MIT — Use freely, commercial or otherwise.
🎯 Next Steps
- Install now: Run the one-click installer
- Try it:
add_repo()+get_code_city() - Read cookbook: Advanced workflows and examples
- Share feedback: GitHub Issues
Made for developers who demand more from their tools. ⚡
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。