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Glama

agent-trust-oracle

describe_agent

Fleet-standard self-description.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2/5.0
Behavior2/5

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

With no annotations provided, the description bears full responsibility for disclosing behavioral traits. It merely labels the tool as a 'self-description' without explaining side effects, idempotency, or any constraints. The agent learns nothing about what happens when the tool is invoked.

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

Conciseness2/5

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

At 4 words, the description is extremely concise but fails to earn its place by providing meaningful information. It is underspecified, not appropriately sized for an AI agent to understand the tool's role.

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

Completeness2/5

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

Despite having an output schema, the description does not hint at the return format or content. For a simple tool with no parameters, the description should clearly state what information is returned. The current text is insufficient for the agent to form a mental model of the tool's output.

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?

The input schema is empty (0 parameters), and schema description coverage is 100% trivially. The description adds no semantic value beyond the schema; 'self-description' does not explain what the output schema contains or how the tool might be used effectively.

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

Purpose2/5

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

The description 'Fleet-standard self-description' is vague and does not clearly state what the tool does. It suggests a self-description mechanism but lacks specificity about the subject (agent, fleet, or something else) and does not differentiate from sibling tools like history or score_agent.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool or when to consider alternatives. The description does not indicate any prerequisites, conditions, or comparisons with sibling tools, leaving the agent without decision support.

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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TDQS

B3.2/5.0
Disambiguation5/5

Each tool targets a distinct operation: issuing attestations, self-description, history retrieval, outcome recording, score querying, and verification. No overlaps.

Naming Consistency4/5

Tools mostly follow verb_noun pattern (describe_agent, record_outcome, score_agent, verify_attestation), but 'history' is a noun-only exception. Still clear and predictable.

Tool Count5/5

Six tools cover the core trust oracle functionality without bloat. Each tool serves a necessary role in the lifecycle.

Completeness4/5

Covers recording outcomes, querying scores, and managing attestations. Lacks a tool to list agents or modify outcomes, but these are plausible intentional constraints.