MCP Tool Guardian
Protects AI agents from bad tool outputs, schema mismatches, and tool poisoning by validating, sanitizing, and scoring reliability of tool responses.
README
MCP Tool Guardian
Production-ready MCP Server for AI Agent Reliability & Accountability
Protect your AI agents from bad tool outputs, schema mismatches, and potential tool poisoning. Built for entrepreneurs who want safer, more accountable AI systems.
The Problem (Simple Explanation)
Imagine your AI helper is like a 10-year-old kid who asks friends for information. Sometimes the friends give wrong, incomplete, or even tricky answers. The kid then makes mistakes because of that bad information.
MCP Tool Guardian is like a smart teacher who checks every answer the friends give before the kid uses it. It makes sure the answer matches the expected shape, cleans dangerous parts, and gives a reliability score.
This is especially useful for founders building AI products who care about trust and accountability (like Prevalid's mission).
What This MCP Server Does
It exposes tools that any MCP-compatible AI agent (Claude Desktop, Cursor, Windsurf, etc.) can call:
| Tool | What it does (kid-friendly) |
|---|---|
validate_against_schema |
Checks if the data looks exactly like the promised shape (like checking if a LEGO set has all the right pieces) |
sanitize_tool_output |
Cleans the data – removes secrets, weird characters, or possible injection tricks |
score_reliability |
Gives a score from 0-100 how trustworthy the tool answer is |
detect_suspicious_patterns |
Looks for common "poison" patterns that bad tools sometimes hide |
create_audit_entry |
Writes a simple log so you can later see what happened (accountability!) |
Why This is Valuable for Entrepreneurs
- Reduce AI failures: Fewer cascading errors from bad tool data
- Low cost: Pure local computation – no expensive external APIs needed
- Accountability: Perfect for Prevalid-style AI Execution OS
- Ready for production: Stateless design compatible with new MCP 2026-07-28 spec
- Easy to sell: Can be packaged as a paid MCP or internal reliability layer
Quick Start
Requirements
- Python 3.10+
mcppackage (official SDK)
pip install mcp jsonschema pydantic
Run the server
python -m src.server
Or with uv:
uv run python -m src.server
Add to Claude Desktop / Cursor
Add this to your MCP config:
{
"mcpServers": {
"tool-guardian": {
"command": "python",
"args": ["-m", "src.server"],
"cwd": "/path/to/mcp-tool-guardian-20260730"
}
}
}
Project Structure
mcp-tool-guardian-20260730/
├── src/
│ ├── __init__.py
│ ├── server.py # Main MCP server
│ ├── tools/
│ │ ├── __init__.py
│ │ ├── validate.py
│ │ ├── sanitize.py
│ │ ├── reliability.py
│ │ └── audit.py
│ └── utils/
│ └── schemas.py
├── tests/
│ └── test_tools.py
├── mcpize.yaml
├── .env.example
├── pyproject.toml
├── LAUNCH.md
└── README.md
License
MIT – Build freely, stay accountable.
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。