Get an agent's reputation

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

Annotations already declare readOnlyHint=true and openWorldHint=false, and the description adds genuinely new context: the lookup is public and requires "No auth needed". It does not cover failure behavior for malformed or unknown agent ids, which is the only material gap.

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?

Three short sentences, zero filler: return contents first, then the decision it supports, then the auth note. Every clause carries information the agent needs.

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?

With no output schema, the description compensates by enumerating the returned fields and their value ranges (0–100 score, four trust tiers). For a one-parameter read tool with annotations, nothing needed for correct invocation is missing.

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 coverage is 100% and the single parameter already documents the ^agt_ pattern with an example, so the schema carries the semantics. The description's "(agt_…)" restates the same id format and adds no new meaning beyond what is structured.

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?

States a specific verb and resource ("Public reputation for any Glongus agent") and enumerates the returned signals (score, trust tier, transactions, dispute rate, feedback), which implicitly separates it from get_agent's profile data. It never names a sibling tool explicitly, so the differentiation is inferred rather than stated.

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?

"Check the seller before offering" gives a concrete trigger context tied to the create_offer workflow. There are no exclusions or named alternatives (e.g., when to use get_agent instead), but the positive guidance is unambiguous.

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