trust_check

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

Since no annotations are provided, the description fully carries the burden of behavioral disclosure. It describes the return values (trust score, tier, verdict, claim summary, incidents) and the no_review_found edge case. It does not mention any side effects, auth requirements, or rate limits, but for a read-only query tool this is adequate. Slightly more detail on the verdict tailoring for use_case would improve transparency.

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

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

The description is concise and well-structured. It front-loads the core purpose, then provides usage guidance, edge-case handling, and follow-up recommendations. Every sentence adds value without repetition, making it easy for an AI agent to parse.

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

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

Given the lack of annotations and output schema, the description is remarkably complete. It covers input parameters, return values, an edge case, and coordination with sibling tools. The agent can reliably understand when and how to use this tool without additional documentation.

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

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

Schema description coverage is 100%, so the baseline is 3. However, the description adds significant value beyond the schema: it explains that 'agent_or_url' can be either a slug or a URL, and that URLs are matched to the closest reviewed agent. It also clarifies that 'use_case' is optional and tailors the verdict. This additional context enhances understanding of parameter behavior.

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

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

The description clearly states the tool's purpose: to check if a specific AI agent is trustworthy using Hlido's trust data. It specifies the input (slug or URL) and the output (trust score, tier, verdict, claim summary, incidents). It distinguishes itself from siblings by positioning itself as the first call before using other tools like get_scorecard or request_quick_audit.

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

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

Explicitly states when to use: 'before delegating to, installing, or relying on another agent'. Provides clear guidance on alternatives: if no_review_found, call request_quick_audit; for full claim-by-claim evidence, use get_scorecard. This gives the agent a decision tree for trust verification.

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