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

With no annotations, the description must disclose behavior itself. It does list the return fields and implies a read-only probe, but it does not explicitly state safety, side effects, or interpretation of 'healthy' vs 'stale'. The term 'probe' hints at non-mutation, but deeper behavioral context is absent.

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 two sentences: a concise definition with a colon-separated field list, followed by a clear call-condition. It is front-loaded, uses no filler, and every word 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?

For a simple no-parameter tool with no output schema, the description is fairly complete: it lists the output fields and gives the trigger context. It does not explain each field's meaning or how to judge health from the values, but the field names are self-descriptive and the tool is minimal.

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?

The tool has zero parameters, so the description carries no parameter burden. The baseline for 0 params is 4, and the description does not need to add parameter semantics. It does mention fields that are outputs, not inputs.

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 identifies the tool as an 'ML pipeline freshness probe' and lists its specific fields (has_predictions, has_weights_history, latest_as_of_ts, n_tickers). It also states exactly when to use it: 'when the user asks whether the ML pipeline is healthy or why predictions look missing/stale.' This distinguishes it from sibling tools like tengu_ml_predict or tengu_ml_weights.

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 gives explicit usage context: 'Call when the user asks whether the ML pipeline is healthy or why predictions look missing/stale.' This tells the agent when to invoke it, but it does not name alternative tools or specify when not to use it, so it falls short of the highest bar.

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