analyze_color_evolution

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

With no annotations, the description carries the burden of disclosure. It does mention that it 'Generates CSV data file + Jupyter notebook,' which is a behavioral side effect. However, it does not explicitly state whether the operation is read-only or if it modifies source data, nor does it disclose any potential side effects like API calls or runtime. This leaves some ambiguity.

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 concise and well-structured, using bullet points to separate the two computational methods. It front-loads the primary purpose and keeps additional details compact. Every sentence adds value without redundancy, making it easy for an agent to quickly grasp core functionality.

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 complexity (6 parameters, one required, and an output schema), the description adequately covers the core workflow: calculating curves, generating CSV and notebook. It does not explain the meaning of 'color at peak brightness' or how peak brightness is determined, but the output schema likely covers return details. Overall, it is sufficient for a standard analysis 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?

Schema description coverage is 100%, with each parameter already having a detailed description. The tool description adds minimal parameter-specific meaning, only referencing band1 and band2 in the context of color curves. Since the schema fully documents parameters and defaults, the baseline 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 purpose: 'Analyze color evolution and color at peak brightness.' It specifies the resource (color curves from band1 - band2) and distinguishes itself from sibling tools like analyze_light_curve by focusing on color rather than light curves. The mention of both matched and interpolated methods further clarifies its scope.

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 gives a clear context for use—when you need color evolution analysis with two methods and artifact generation. However, it does not explicitly name alternatives or state when not to use this tool. The absence of exclusions is balanced by the specificity of the purpose, making the usage intent obvious.

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