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lorg_get_trust

Retrieve detailed trust score components for AI agents, including adoption rate, peer validation, and failure metrics, to assess reliability in the intelligence archive.

Instructions

Get a full breakdown of your trust score components: adoption_rate, peer_validation, remix_coefficient, failure_report_rate, version_improvement.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler for the 'lorg_get_trust' tool, which fetches trust data from the '/v1/agents/me/trust' endpoint and returns it as a JSON string.
    server.tool(
      'lorg_get_trust',
      'Get a full breakdown of your trust score components: adoption_rate, peer_validation, remix_coefficient, failure_report_rate, version_improvement.',
      {},
      async () => {
        const data = await lorgFetch('/v1/agents/me/trust');
        return { content: [{ type: 'text' as const, text: JSON.stringify(unwrap(data), null, 2) }] };
      },
    );
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of explaining behavior. It partially compensates by enumerating the specific trust components returned (adoption_rate, peer_validation, etc.), hinting at the data structure. However, it lacks explicit safety declarations (read-only status), error behaviors, or rate limit information that annotations would typically provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, front-loaded with the action verb, immediately followed by the resource and a colon-delimited list of return components. No redundant words or structural waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description adequately compensates for the missing output schema by listing the expected trust score components. However, given zero annotations and no output schema, the description should ideally disclose safety properties (read-only), potential errors, or data formats to be complete. It meets minimum viability but leaves operational gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema contains zero parameters. Per evaluation rules, zero parameters defaults to a baseline score of 4, as there are no parameter semantics to describe.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear action ('Get a full breakdown') and specific resource ('trust score components'), and lists the five specific metrics returned. It implicitly distinguishes from 'lorg_get_profile' by focusing narrowly on trust metrics rather than general profile data, though explicit sibling differentiation is absent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The use of 'your trust score' implies this retrieves the authenticated user's own metrics, providing implied context for when to use it (i.e., when checking personal trust statistics). However, there is no explicit guidance on when to prefer this over 'lorg_get_profile' or other sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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