RepoGraph-Honest MCP Server
An MCP server that validates generated code against project structure and installed dependencies, catching undefined symbols, wrong API calls, dead code, and type mismatches in real time. It provides tools for project indexing, symbol/API checking, sandboxed execution, file scanning, and code analysis.
README
RepoGraph-Honest MCP Server
A lightweight Model Context Protocol (MCP) server that catches code hallucinations in real time — undefined symbols, wrong library API calls, dead code, and obvious type mismatches — by verifying generated code against your project structure and installed dependencies.
RepoGraph-Honest is a self-contained extraction of the "honest action routing" idea. Instead of trusting a code model blindly, it checks the symbols and APIs the model emits before they reach your editor.
It is not a heavyweight ML system: it needs only mcp, tree-sitter, and
tree-sitter-python — no torch / transformers.
Table of contents
- Features
- Installation
- Running the server
- Connecting to an MCP client
- Tool reference
- Typical workflow
- Architecture
- Development
- Sandbox security
- Contributing
- License
Features
| Capability | Tool | What it does |
|---|---|---|
| Project indexing | index_project |
Builds a symbol index (functions / classes / variables) from a directory; caches results across calls |
| Dependency APIs | load_project_deps |
Loads public API signatures from requirements.txt / pyproject.toml |
| Symbol check | check_symbol |
Verifies an identifier is defined in the project |
| API check | check_api |
Verifies a library API call is correct (e.g. pd.read_exel → suggestions) |
| Sandbox exec | execute_code |
Runs code in a subprocess sandbox; returns stdout/stderr + error type |
| File scan | scan_file |
Scans a whole file for undefined calls |
| Package APIs | load_package_apis |
Loads/caches API signatures for one package |
| Stats | get_project_stats |
Index statistics |
| Type check | validate_types |
Lightweight AST-based structural checks (None iteration, wrong arg counts, etc.) |
| Dead code | find_dead_code |
Finds symbols that appear unused; supports entrypoints and ignore patterns |
| Similar code | find_similar_code |
Finds function-level code clones across the project |
| Call graph | explore_call_graph |
Explores callers and callees of a symbol |
| Search | search_code |
Regex search across project source files |
| Tool routing | choose_tool |
Maps a natural-language query to the best tool |
Installation
# from source
git clone https://github.com/Fengrru/repograph-honest-mcp.git
cd repograph-honest-mcp
pip install -e .
# or just the runtime deps
pip install -r requirements.txt
Python ≥ 3.10 is required.
Running the server
# as a module (stdio transport — what MCP clients expect)
python -m repograph_honest.mcp.server
# via the launcher script
python scripts/run_mcp_server.py
# HTTP/SSE transport (optional)
python scripts/run_mcp_server.py --transport sse --host 127.0.0.1 --port 8000
Connecting to an MCP client
Add this to your client's MCP configuration (Cursor, Claude Desktop, VS Code, etc.):
{
"mcpServers": {
"repograph-honest": {
"command": "python",
"args": ["-m", "repograph_honest.mcp.server"],
"cwd": "/absolute/path/to/repograph-honest-mcp"
}
}
}
After restarting the client, the tools above become available.
Tool reference
All tools are exposed by the MCP server. They can also be called directly from Python
(see examples/).
index_project(root_path: str, force_rebuild: bool = False) -> dict
Build (or reuse) the project symbol index.
index_project("/path/to/project")
# => {"success": True, "symbols_indexed": 42, "root": "...", "cached": False}
load_project_deps(root_path: str) -> dict
Parse requirements.txt or pyproject.toml and load the public API signatures of
listed packages.
load_project_deps("/path/to/project")
# => {"success": True, "packages_loaded": ["requests", "pytest"], "total_apis": 1204}
check_symbol(symbol_name: str, file_path: str | None = None) -> dict
Check whether a symbol is defined in the indexed project.
Symbols are stored with their full module-qualified name, e.g. pkg.core.main.
check_symbol("pkg.core.main")
# => {"success": True, "symbol": "pkg.core.main", "defined": True, "location": {...}}
check_api(api_name: str) -> dict
Check whether a library API exists and get suggestions for typos.
check_api("math.sqrt") # valid
check_api("math.sqrtt") # invalid + suggestions
execute_code(code: str, prelude: str = "", known_names: list[str] | None = None) -> dict
Run code in a fresh subprocess with a temporary working directory and timeout.
execute_code("print(1 + 1)")
# => {"success": True, "output": "2", ...}
scan_file(file_path: str) -> dict
Scan a file for undefined calls using AST analysis.
scan_file("/path/to/project/bad.py")
# => {"success": True, "issues": [{"type": "undefined_call", "name": "...", "line": 7}]}
validate_types(code: str) -> dict
Lightweight structural checks on a code snippet:
- iterating over
None - wrong argument counts for common builtins (
len,sum, etc.) - calling constant values
- string methods on non-string constants
validate_types("for x in None:\n pass")
# => {"success": True, "issues": [{"type": "none_iteration", ...}]}
find_dead_code(entrypoints: list[str] | None, ignore_patterns: list[str] | None, include_tests: bool = True) -> dict
Find symbols that appear unused. Provide entrypoints to keep known roots alive.
find_dead_code(entrypoints=["pkg.cli.main"])
# => {"success": True, "dead_symbols": [...], "count": 3}
explore_call_graph(symbol_name: str) -> dict
Return definitions, callers, and callees of a symbol.
explore_call_graph("pkg.core.helper")
# => {"success": True, "callers": [...], "callees": [...]}
search_code(pattern: str, glob: str = "*.py") -> dict
Regex search across project source files.
search_code(r"def \w+_helper")
Typical workflow
index_projecton your repo root → builds the symbol table (cached).load_project_deps→ loads dependency APIs.- Ask your coding agent to generate code; before accepting, it can:
check_symbol("pkg.core.my_helper")→ ensure it really exists,check_api("pandas.read_csv")→ confirm the API name,execute_code(...)→ actually run the snippet and surface errors,find_dead_code()→ detect newly orphaned code.
Architecture
repograph-honest-mcp/
├── repograph_honest/
│ ├── mcp/ # FastMCP server + tool implementations
│ │ ├── server.py # entry point (mcp.run)
│ │ ├── tools.py # tool logic
│ │ └── knowledge_base.py # installed package API cache
│ ├── honest/ # honest action routing
│ │ ├── router.py # HonestRouter + ToolIntent routing
│ │ └── symbol_index.py # project-wide symbol index + cache
│ ├── structure/ # tree-sitter based extraction
│ │ ├── extractor.py
│ │ └── relations.py
│ └── sandbox/ # sandboxed execution
├── scripts/
│ └── run_mcp_server.py
├── tests/
├── .github/workflows/ # CI
│ └── ci.yml
├── pyproject.toml
├── requirements.txt
└── README.md
Key design decisions:
- AST-first: call graphs and file scans use
astinstead of fragile regex. - Module-qualified symbols: the index stores
pkg.module.funcso cross-file references are unambiguous. - Lazy loading + caching: dependency APIs and project indices are cached and invalidated by content hash.
- Thread-safe global state: tool state is protected by a lock so concurrent MCP requests do not race.
Development
pip install -e ".[dev]"
pytest
ruff check repograph_honest tests scripts
ruff format repograph_honest tests scripts
See CONTRIBUTING.md for pull-request guidelines.
Sandbox security
execute_code runs in a subprocess with timeout protection, a temporary working
directory, and optional Unix resource limits. It is safe against accidental infinite
loops and simple mistakes, but it is not a hardened security boundary against
malicious code. For untrusted code, run inside a container or dedicated virtual
machine.
Contributing
Contributions are welcome! Please read CONTRIBUTING.md first.
Changelog
See CHANGELOG.md.
License
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。