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

No annotations or output schema are provided, so the description carries the transparency burden. It discloses useful behavioral traits: data comes from the tool's own probes rather than hearsay, no authentication is required, and optional restrictions exist. It does not state whether the result is a single top chain or a ranked list, nor what happens when no chain qualifies.

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 compact sentences with no filler: the purpose and data source are front-loaded, optional filters follow immediately, and the auth note is a useful closing detail. Every sentence earns its place.

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?

The tool is simple and all three optional parameters are fully documented in the schema. However, with no output schema, the description should ideally clarify the result shape—whether it returns one chain slug, a ranked list, or additional metadata—which is currently 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 description coverage is 100%, so the parameters are already well documented. The description restates the family and candidate-list restrictions without adding much semantic value beyond the schema, and the default ranking behavior is already captured in the prefer parameter's schema description.

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 function with a specific verb and resource: 'Recommend a chain from measured data' based on status, uptime, and p50 latency. It is unambiguous about what the tool does, but it does not explicitly differentiate itself from the sibling pick_route, so it stops short of the strongest sibling-distinction score.

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?

The description implies when to use the tool—when a chain should be chosen from measured data—and mentions optional filtering by family or candidate list. However, it gives no explicit guidance about when not to use it and does not mention alternatives such as pick_route, leaving the decision largely to inference.

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