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trustscoreagent/trustscoreagent

by Various

Free, open reputation registry for AI microservices. Agents check trust scores before calling any service.

MCP

trustscoreagent/trustscoreagent

Added 3 Sept 2026

Overview

TrustScoreAgent is a free, open-source reputation registry for AI microservices, implemented in C#. It enables agents to query trust scores for services before invoking them, providing a decentralized mechanism for assessing service reliability.

Best for

Best for
Developers building AI agent systems that need a lightweight, open trust verification layer for microservices.

Use cases

  • Allow AI agents to verify service trust scores before making API calls
  • Publish and maintain reputation metrics for microservices in a shared registry
  • Integrate trust checks into agent decision-making workflows for service selection

Notes

TrustScoreAgent is a free, open-source reputation registry for AI microservices, implemented in C#. It enables agents to query trust scores for services before invoking them, providing a decentralized mechanism for assessing service reliability.

0 stars on GitHub. Last updated 2026-08-30. Licensed Apache-2.0.

Use cases

  • Allow AI agents to verify service trust scores before making API calls
  • Publish and maintain reputation metrics for microservices in a shared registry
  • Integrate trust checks into agent decision-making workflows for service selection

Pros

  • Open source and free to use
  • Built in C#, fitting .NET ecosystems
  • Addresses a real need for trust in AI service interactions

Cons

  • No current stars or community traction, indicating early-stage adoption
  • Limited documentation and ecosystem support
  • Reputation model may require significant customization for diverse use cases

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Open source and free to use
  • Built in C#, fitting .NET ecosystems
  • Addresses a real need for trust in AI service interactions

Cons

  • No current stars or community traction, indicating early-stage adoption
  • Limited documentation and ecosystem support
  • Reputation model may require significant customization for diverse use cases
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