get_logs

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 only states it returns runtime logs without mentioning permissions, side effects, or whether the server must be running. The description adds little beyond the obvious read operation.

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?

Two sentences: the first defines the tool concisely, the second adds context with concrete examples. No filler or redundancy.

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?

The description lacks return format details (e.g., plain text or JSON, line-by-line) and does not clarify default behavior for optional parameters. For a simple log retrieval tool with no output schema, more context is needed.

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 coverage is 100%, so baseline is 3. The description adds no additional meaning to the parameters beyond what the schema already provides (max_lines and keyword with examples).

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 explicitly states 'Get ComfyUI server runtime logs' with a clear verb and resource. It further specifies use cases like debugging errors and missing nodes, distinguishing it from siblings such as generate_image or analyze_workflow.

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 specific debugging scenarios (execution errors, model loading issues) indicating when to use, but does not mention when not to use or compare to alternatives like analyze_workflow for error analysis.

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