prepare_run_summary

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

No annotations are provided, so the description fully handles transparency. It discloses that no server-side LLM call occurs and that Claude in the client writes the report. It does not mention side effects or auth needs, but given the read-only nature of the tool, this is acceptable.

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 three sentences, each earning its place: purpose, architectural note, and parameter guidance. No fluff, well structured, and front-loaded with the core action.

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 low complexity (2 parameters, no output schema), the description covers all needed aspects: what the tool does, how it works architecturally, and parameter usage. It is sufficiently complete for an AI agent to select and invoke correctly.

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?

With 0% schema description coverage, the description fully compensates by explaining the purpose of 'include_passed_titles' and providing guidance on when to set it to true. It adds significant meaning beyond the bare schema.

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 it pulls run metadata, statuses, and failure comments and returns a structured payload with report instructions. It uses specific verbs ('pull', 'return') and identifies the resource, making it easy to distinguish from siblings like get_run or get_results_for_run.

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 provides context on when to use the boolean parameter and the default behavior, and it explains that no LLM call happens server-side. It implicitly suggests use for summary generation but does not explicitly state when not to use it or list alternatives. Still, it gives actionable guidance.

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