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

There are no annotations, and the description only says 'Compute', which implies a pure calculation but does not explicitly state that it has no side effects, whether it requires authentication, or how invalid inputs are handled. It does not disclose edge-case behavior such as zero-count division.

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 a single concise sentence with no redundant phrasing. It efficiently conveys the core computation and output components.

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?

No output schema is provided, and the description only names the outputs without specifying their types, order, or format. It also does not clarify whether the optional parameters are required to produce meaningful results or what happens when only 'mentionsCount' is supplied.

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 schema provides parameter names and minimum values but no descriptions, and the description only loosely ties the inputs to 'mention counts' and outputs. It does not explain the role of 'founderMentions' or exactly how 'recent7dCount', 'sentimentPositive', and 'sentimentNegative' map to recency share and positivity rate.

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 a specific action ('compute') and identifies the resource/result ('x_signal_score (0-20) + recency share + positivity rate') from X/Twitter mention counts. It is distinct from sibling compute_* tools because it specifically targets X/Twitter mention data.

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 implies use when X/Twitter mention counts are available and a signal score is needed, but it does not explicitly state when to prefer this tool over the many sibling compute_* tools. No conditions, prerequisites, or alternative-selection guidance are provided.

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