get_history

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

With no annotations provided, the description fully carries the behavioral disclosure burden. It does so admirably: repeatedly states read-only guarantees, clarifies that stats/suggest use a LOCAL database not ComfyUI, explains the fallback to most recent failed/successful run, warns about lag after a run finishes, and notes that stats return empty until generations exist. No contradictions with annotations since none exist.

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 long but inherently complex with four actions and six parameters. It front-loads the core purpose in the first sentence, then uses clear bullets and action-specific subsections. Every sentence delivers distinct information: edge cases, fallbacks, data-source distinctions, and cross-tool links. No wasted words or filler.

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?

For a multi-action tool with no output schema and no annotations, this description is exceptionally complete. It covers return values (status, timing, filenames, error tracebacks, missing models, node types, statistics), behavioral nuances (empty stats, lag, fallback logic), and prerequisites (works without ComfyUI for stats/suggest). It also provides actionable next steps for failure diagnosis, making it fully self-contained.

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?

Although the schema already covers 100% of parameters, the description adds substantial semantic value: it maps each parameter to specific actions, explains defaults (limit default 10, prompt_id omission behavior), clarifies that prompt_id behaves differently for list vs diagnose, and gives example values (model_family: 'sdxl', 'flux'). It also explains which parameters interact with which actions, going far beyond the schema's short descriptions.

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 opens with a clear verb+resource statement: "Read what has already been generated on this machine" and then enumerates four specific actions (list, diagnose, stats, suggest) each with a distinct purpose. It clearly differentiates from siblings like get_image and get_workflow by focusing on execution history, failures, and settings usage.

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 explicit when-to-use guidance: 'To diagnose WHY a run FAILED or what is missing, prefer action:"diagnose"', 'Use action:"list" when you need the run's OUTPUTS or timing', and 'Call this whenever a run fails... instead of guessing from raw logs.' It also names alternative tools for follow-up actions (download_model, search_custom_nodes) and contrasts stats vs suggest. This is model-level guidance.

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