atlas_score_v2

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

No annotations are provided, so the description carries the full burden. It explains the weighting and the bug fix, which adds transparency about behavior. However, it does not cover edge cases, data requirements, or what happens with missing data. The basic behavior is clear but not exhaustive.

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, fitting key information in one sentence. It front-loads the purpose and then lists components. However, the dense format could benefit from bullet points for clarity, but it is not overly verbose.

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 tool with no parameters and no output schema, the description explains the input (implicitly country data) and the formula but does not describe the output format or return value. It also omits context like data freshness, assumptions, or limitations, making it somewhat incomplete.

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 tool has zero parameters, so the baseline is 4 per guidelines. The description adds value beyond the schema by detailing the scoring logic (components, weights), which helps an agent understand the tool's computation without parameters.

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 defines the tool as a level-aware composite score for censorship risk, breaking down each component with percentages. It specifies the purpose (computing a composite score for countries) and differentiates from v1 by noting the bug fix, which distinguishes it from siblings like atlas_score.

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?

Usage is implied: the tool computes a composite score for countries, likely when assessing censorship risk. However, no explicit guidance on when to use this vs. alternative tools (e.g., atlas_score, atlas_voidly_score) or when not to use it. Siblings exist but no distinctions are made.

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