analyze_light_curve

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

With no annotations, the description takes on the transparency burden. It clearly states side effects and outputs: 'Generates CSV data file + Jupyter notebook with interactive Plotly plots plus editable analysis code cells.' It also explains behavior for incomplete light curves and the baseline_threshold parameter semantics. However, it doesn't explicitly state that it is a read-only analysis or mention any system-level effects, leaving a small gap.

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 well-structured with a strong lead sentence, a clear list of computed properties, a bulleted summary of edge-case handling, and explicit output details. Each section earns its place without redundancy, and the overall length is appropriate 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 the tool's moderate complexity and the presence of an output schema, the description covers all necessary selection and invocation context: what it does, how parameters affect output, edge cases, and what artifacts it produces. An agent can confidently choose this tool and understand its behavior.

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 the baseline is 3. The description adds meaningful context beyond the schema by linking parameters to analysis behavior, such as 'for each requested photometric band' and explaining baseline_threshold in the main description. It also gives concrete filter string examples, which help agents form correct values.

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 'Analyze light curve evolution for multiple photometric bands,' using a specific verb and resource. It clearly states the computed quantities (rise time, fade time, duration, pre-peak variability) and differentiates from siblings like get_source_photometry (raw photometry) and analyze_color_evolution (color analysis).

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 context on when to use the tool by explaining how incomplete light curves are handled (still rising, still fading, returned to baseline) and what output formats are available. It does not explicitly name alternative tools or exclusion criteria, but the use cases are well implied.

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