Trust and reputation scores for AI agent service selection
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# TrustScore [](https://github.com/bensargotest-sys/bensargotest-sys/actions/workflows/ci.yml) **Trust and reputation scores for AI agent service selection.** TrustScore is an MCP server that helps AI agents make better decisions about which service providers to trust. Think of it as a credit score for APIs, agents, and tools. ## Prerequisites - **Python 3.10 or higher** ([download](https://python.org)) - pip 20.0+ (included with Python) - 10GB disk space (for database and logs) - Internet connection (for initial database seeding) Verify your setup: ```bash python3 --version # Should show 3.10+ pip3 --version # Should show 20.0+ ``` ## Quick Start ```bash # 1. Clone repository git clone https://github.com/bensargotest-sys/trustscore.git cd trustscore # 2. Create virtual environment (recommended) python3 -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate # 3. Install package pip install -e . # 4. Seed database with 200+ MCP servers python scripts/seed_database.py # 5. Run as MCP server python -m src.server # 6. Verify it's working python -m tests.test_basic ``` ## What Problem Does This Solve? AI agents need to call external services: APIs, other agents, databases, DeFi protocols. But how do you know which ones are reliable? - **Uniswap** might have 99% uptime - **SketchySwap** might fail 80% of the time - **SlowAPI** might timeout constantly Without trust data, agents pick randomly or rely on hard-coded preferences. TrustScore fixes this with real behavioral data. ## How It Works 1. **Agents report outcomes** after calling a service 2. **TrustScore aggregates** success rates, latency, failure patterns 3. **Other agents query** TrustScore before picking a provider 4. **Network effect:** More users = better data = better decisions ## MCP Tools ### `trustscore_rank` Rank multiple providers by trust score. ```json { "provid…
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