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

With no annotations, the description carries the burden but does disclose one important behavioral trait the schema cannot: this call consumes builder_call credits. It says nothing about whether it is read-only, latency, or whether the conversation is stateful across calls.

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?

One short sentence, front-loaded with the action, with the cost caveat in parentheses. Nothing wasted, though the brevity is partly under-specification rather than efficiency.

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?

No annotations, no output schema, and three undocumented required parameters. For a credit-consuming AI call, the description should indicate what the response looks like (analysis text? chat continuation?) and whether follow-up questions are permitted.

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 0% for all three required parameters (pipeline_id, run_id, question). The description never explains the relationship between pipeline_id and run_id, nor what kind of question text is expected, so nothing compensates for the schema gap.

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+resource: ask the AI to analyse a finished run. The word 'debug' in the name and 'analyse a finished run' in the description separate it from pipeline_builder_chat, though the sibling alternative is never named explicitly.

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 phrase 'finished run' implies the precondition that the run must be complete before asking, but there is no explicit when-to-use/when-not guidance and no routing toward pipeline_builder_chat or pipeline_run_get for other diagnostics.

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