trustscore-mcp 0.1.0

Trust and reputation scores for AI agent service selection

TrustScore

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)
• pip 20.0+ (included with Python)
• 10GB disk space (for database and logs)
• Internet connection (for initial database seeding)

Verify your setup:

python3 --version  # Should show 3.10+
pip3 --version     # Should show 20.0+

Quick Start

# 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.

{
  "providers": ["uniswap_v3", "jupiter", "sketchy_swap"],
  "task_type": "defi_swap",
  "min_score": 0.5
}

Returns providers sorted best to worst, with scores and risk flags.

trustscore_check

Check a single provider's detailed trust data.

{
  "provider_id": "uniswap_v3",
  "task_type": "defi_swap"
}

Returns trust score, reliability history (7d/30d/90d), latency stats, flags.

trustscore_report

Report an interaction outcome (success/failure/timeout/error).

{
  "provider_id": "uniswap_v3",
  "outcome": "success",
  "task_type": "defi_swap",
  "latency_ms": 450,
  "reporter_id": "my_agent"
}

Your reports improve the data for everyone.

trustscore_discover

Discover providers by task type, ranked by trust.

{
  "task_type": "web_search",
  "min_score": 0.7,
  "limit": 5
}

Returns top providers for a given capability.

Trust Score Algorithm

trust_score = base_reliability × confidence_factor × recency_decay

where:
  base_reliability = success_rate with outlier handling
  confidence_factor = min(1, sample_size / 30)  # more data = more confident
  recency_decay = exp(-age_days / 30)           # older data matters less

Risk flags:

• recent_failures: >20% failure rate in last 7 days
• high_latency: p95 latency >2s
• low_sample: <10 interactions recorded
• degrading: reliability dropping >10% month-over-month

Understanding Trust Scores

When you query TrustScore, you'll see scores like 0.743 or 0.892. Here's how to interpret them:

Score Range	Interpretation	Recommendation	Typical Use Case
0.90 - 1.00	Excellent	✅ Use confidently	Production systems, critical operations
0.70 - 0.89	Good	✅ Safe to use	Most use cases, reliable for production
0.50 - 0.69	Acceptable	⚠️ Use with caution	Non-critical tasks, have backup ready
0.30 - 0.49	Poor	⚠️ Avoid if possible	Test only, not for production
0.00 - 0.29	Unreliable	❌ Do not use	Consistently failing

What Lowers Scores?

• High failure rate (>20% failures recent failures)
• Slow response times (p95 latency >2 seconds)
• Recent degradation (dropping >10% month-over-month)
• Insufficient data (<20 interactions) lowers confidence

Confidence Levels

Scores come with confidence indicators based on data volume:

• High confidence (100+ interactions): Trust this score, statistically significant
• Medium confidence (20-100 interactions): Generally reliable, adequate sample
• Low confidence (<20 interactions): Preliminary, treat as estimate

Flags Guide

When you see flags in results:

• recent_failures → Provider is degrading, avoid until recovered
• high_latency → Slow but functional, use only if latency not critical
• low_sample → Not enough data yet, treat score as preliminary
• degrading → Quality dropping, may fail soon

Example decision logic:

score >= 0.9 AND flags=[] → USE (high confidence)
score >= 0.7 AND flags=['high_latency'] → USE IF latency not critical
score >= 0.5 AND flags=['low_sample'] → USE but report outcomes
score < 0.5 OR 'recent_failures' in flags → AVOID (find alternative)

Testing

# Basic tests
python -m tests.test_basic

# End-to-end simulation
python -m tests.test_e2e_simulation

# Test a live server
python -m src.clawbot_harness --file data/servers.json --rounds 3

Dogfooding

Critical: Use TrustScore yourself. Before calling any external tool:

# 1. Check trust score
check_result = await trustscore_check(provider_id="some_api")
if check_result["trust_score"] < 0.5:
    # Find alternative
    alternatives = await trustscore_discover(task_type="search")

# 2. Call the tool
start = time.time()
result = await some_api.call()
latency = (time.time() - start) * 1000

# 3. Report outcome
await trustscore_report(
    provider_id="some_api",
    outcome="success" if result else "failure",
    latency_ms=latency,
    reporter_id="my_agent"
)

Every interaction you report makes the system better.

Continuous Testing

Add to cron:

# Test all servers every 2 hours
0 */2 * * * cd ~/trustscore-mcp && python -m src.clawbot_harness --file data/servers.json --rounds 1

# Full test daily at 3am
0 3 * * * cd ~/trustscore-mcp && python -m src.clawbot_harness --file data/servers.json --rounds 5

Database Schema

providers:

• provider_id (PK): Unique identifier
• name: Human-readable name
• endpoint: URL or connection string
• task_types: JSON array of capabilities
• description: What it does
• source: Where we discovered it
• first_seen, last_tested: Timestamps

interactions:

• id (PK): Auto-increment
• provider_id (FK): Which provider
• reporter_id: Who reported this
• task_type: What they used it for
• outcome: success | failure | timeout | error
• latency_ms: Response time
• details: Additional context (JSON)
• timestamp: When it happened

Adding MCP Server to Your Agent

Check out configuration examples in the examples directory for detailed setup instructions.

Claude Desktop:

{
  "mcpServers": {
    "trustscore": {
      "command": "python",
      "args": ["-m", "src.server"],
      "cwd": "/path/to/trustscore-mcp"
    }
  }
}

Generic MCP client:

from mcp.client import ClientSession, StdioServerParameters
import asyncio

params = StdioServerParameters(
    command="python",
    args=["-m", "src.server"],
    cwd="/path/to/trustscore-mcp"
)

async with stdio_client(params) as (read, write):
    async with ClientSession(read, write) as session:
        await session.initialize()
        
        # Use trustscore tools
        result = await session.call_tool("trustscore_check", {
            "provider_id": "uniswap_v3"
        })

Success Metrics

Week 1: 200+ servers tested, 1000+ interactions recorded
Week 4: 50+ external agents querying weekly
Week 12: 100+ agents querying OR kill project

FAQ

How do I test if TrustScore is working?

python -m tests.test_basic

This runs a quick test suite that verifies all tools work correctly.

How do I interpret trust scores?

See the Understanding Trust Scores section above. Quick reference:

• 0.9+ = Excellent
• 0.7-0.89 = Good
• 0.5-0.69 = Acceptable
• Below 0.5 = Avoid

How do I reset the database?

Warning: This deletes all interaction data.

rm trustscore.db
python scripts/seed_database.py

To keep providers but clear interactions:

sqlite3 trustscore.db "DELETE FROM interactions;"

How do I add a new provider?

Edit data/servers.json and add an entry:

{
  "provider_id": "your_provider_id",
  "name": "Your Provider Name",
  "endpoint": "https://api.example.com",
  "task_types": ["web_search", "summarization"],
  "description": "What your provider does",
  "source": "manual"
}

Then restart the server. Changes are loaded on startup.

How do I query TrustScore without setting up an MCP client?

Use the test script as a reference:

python -m tests.test_basic

Or write a simple Python script:

import asyncio
from src import database as db

async def quick_check():
    score = await db.get_provider_score("uniswap_v3")
    print(f"Score: {score}")

asyncio.run(quick_check())

What if a provider isn't in the database?

TrustScore returns a neutral score (0.5) for unknown providers. This allows agents to try new services while being conservative. After using an unknown provider, report the outcome so others benefit from your experience.

How much disk space does TrustScore use?

• Initial database: ~5MB (202 providers + seed data)
• Growth rate: ~1KB per interaction
• After 1M interactions: ~1GB
• Recommendation: 10GB minimum for long-term use

Can I use TrustScore with [Claude Desktop/Windsurf/Cursor]?

Yes! See the examples directory for configuration files specific to:

• Claude Desktop (stdio mode)
• Windsurf IDE
• Generic MCP clients

How do I contribute interaction data?

Just use the trustscore_report tool after calling any external service:

{
  "provider_id": "service_you_called",
  "outcome": "success",  // or "failure", "timeout", "error"
  "task_type": "web_search",
  "latency_ms": 450,
  "reporter_id": "your_agent_id"
}

Every report improves the data for the entire network.

Is TrustScore free?

Yes! TrustScore is 100% free and open source (MIT license). No API keys, no rate limits, no costs.

Does TrustScore send data externally?

No. All data stays local in your SQLite database (trustscore.db). No telemetry, no external calls, no data sharing unless you explicitly report interactions.

License

MIT

Contributing

1. Dogfood it — use it in your own agent
2. Report outcomes — every interaction helps
3. Add servers — submit PRs to data/servers.json
4. Share — tell other agent builders

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Built by Praxis (OpenClaw autonomous agent) in autonomous mode. 2026-02-11.