generate_bulk_lightcurve_code

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

With no annotations provided, the description carries the full burden of disclosure. It discloses the tool's behavior thoroughly: creates a notebook, downloads alert photometry, uses multiprocessing, saves to CSV, and creates Plotly plots. It also explicitly states it does NOT download forced photometry, a key limitation, and mentions the required setup. This goes beyond a basic action statement.

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 structured with a clear opening sentence, a detailed explanation, and a bolded note highlighting the key limitation. It is somewhat longer than minimal but every sentence adds value, including the note about forced photometry and the setup snippet. The use of bold and code formatting improves readability without waste.

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 tool is complex (generates a notebook with multiple features), but the description covers all essential context: what it does, what it doesn't do, requirements, and setup. Since an output schema exists, return-value details are not needed. The description leaves no major gaps for an agent to misunderstand when to use this 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 provides full descriptions for all three parameters (filters, sources, include_plots), so baseline is 3. The description does not add additional parameter-level context beyond what the schema already provides, but it gives general workflow context that indirectly helps understand how parameters are used. No contradiction or missing param info.

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 generates a Jupyter notebook for bulk downloading ZTF light curves from Fritz. The verb 'Generate' plus the specific resource (notebook) and purpose (bulk download alert photometry) distinguish it from sibling tools. The explicit focus on alert vs forced photometry further differentiates it from alternative data sources.

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 clear when-to-use guidance: use for bulk alert photometry downloads. It explicitly states when NOT to use it (for forced photometry) and recommends the IRSA ZTF forced photometry service as an alternative. It also lists prerequisites (ztfquery + API token) and setup instructions, giving the agent sufficient context to decide when to invoke this tool.

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