github_analyze_failure

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

No annotations are provided, so the description must disclose behavioral traits. It does reveal that the tool uses AI and returns probable cause, suggested fixes, severity, and a diagnostic report. However, it omits potential side effects like cost, latency, or token usage, and does not state whether the operation is read-only. The disclosure is partial, not comprehensive.

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 a single, well-structured sentence that front-loads the core function and lists the expected outputs. Every word contributes value, with no unnecessary repetition or filler. It is appropriately sized for the tool's complexity.

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 there is no output schema, the description adequately summarizes the return values (probable cause, fixes, severity, report). It covers the essential purpose and outputs. However, it lacks detail on the diagnostic report's structure or any constraints (e.g., run must be completed, API limits), which would make it more complete for an AI-driven tool.

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 input schema already fully documents all three parameters (owner, repo, runId) with descriptions, achieving 100% coverage. The tool description adds no extra parameter semantics beyond what the schema provides, so the baseline score of 3 is appropriate.

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: 'Automatically analyze a failed GitHub Actions workflow run using AI.' It names the specific verb 'analyze', the resource 'failed GitHub Actions workflow run', and the approach 'using AI'. This distinguishes it from sibling tools like github_get_workflow_run (retrieval) and github_rerun_workflow (action).

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 usage for failed workflow runs when AI-driven diagnosis is desired, differentiating from simple retrieval or rerun tools. However, it does not explicitly state when not to use it or mention alternatives (e.g., github_get_workflow_logs for raw logs). The context is clear but exclusions are absent.

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