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raditotev

AgentTrust

by raditotev

get_score_breakdown

Retrieve a detailed factor attribution showing how an agent's trust score was computed, including Bayesian raw, dispute penalty, and interaction metrics. Understand the reasoning behind the score.

Instructions

Get a detailed breakdown of how an agent's trust score was computed.

Returns factor attribution showing:

  • bayesian_raw: score before dispute penalty

  • dispute_penalty: multiplier from lost disputes (1.0 = no penalty)

  • interactions_weighted: number of interactions used in computation

  • lost_disputes: count of upheld disputes against this agent

  • alpha/beta: Beta distribution parameters

REQUIRES authentication with trust.read scope. Use this to understand WHY an agent has a particular score.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYes
access_tokenYes
Behavior4/5

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

With no annotations provided, the description fully discloses that this is a read operation, requires trust.read scope, and lists all returned fields with explanations. No contradictions.

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?

The description is concise, uses bullet points for return fields, and front-loads the purpose. Every sentence adds value without redundancy.

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

Completeness4/5

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

Given no output schema, the description thoroughly covers return values. For a tool with two simple parameters and clear authentication requirement, it is sufficiently complete for an agent to use correctly.

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

Parameters2/5

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

Schema coverage is 0%, meaning parameters have no descriptions in the schema. The description does not add any information about 'agent_id' or 'access_token' beyond their names. Given the lack of schema details, the description should compensate but fails to do so.

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

Purpose5/5

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

The description clearly states it returns a detailed breakdown of trust score computation, listing specific fields like bayesian_raw, dispute_penalty, etc. This distinguishes it from siblings like 'check_trust' which likely only return the overall score.

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

Usage Guidelines4/5

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

Explicitly mentions required authentication scope and tells when to use ('understand WHY an agent has a particular score'). However, it does not explicitly state when not to use or mention alternatives among 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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