list_github_api_calls_in_run

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

Since no annotations provided, the description fully discloses behavior: fast (one upstream call), returns calls grouped by job with step/tool attribution, includes top-level and per-job dashboard_url links. No contradictions.

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 concise (2-3 sentences) and well-structured, with clear sections for purpose, usage, and response details. No unnecessary words.

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?

Despite no output schema, the description explains the response structure (grouped by job, dashboard links) and provides actionable tips (include per-job link). Given the complexity, this is fully complete for an agent to use 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?

Input schema has 100% coverage with descriptions for all three parameters. The description adds value by explaining the importance of run_id and suggesting a fallback workflow, but schema already provides the core semantics.

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 lists HTTPS calls to github.com or api.github.com from a specific workflow run, which distinguishes it from sibling tools that handle anomaly detection or general network calls.

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?

Provides explicit usage scenarios: auditing API endpoint usage, detecting unexpected writes. Also gives a prerequisite: if missing run_id, call list_recent_workflow_runs first. This guides the agent on when and how to use the tool.

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