rating_filter

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

With no annotations, the description does not disclose behavioral traits beyond the basic filtering operation. It omits information about rate limits, authentication needs, or whether the tool modifies any state, which is important for a read-like tool.

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 front-loaded with a one-line summary followed by structured parameter explanations. It wastes no words, though the 'Args:' prefix is slightly redundant.

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 presence of an output schema, the description adequately covers input semantics. However, it lacks details on potential errors, case sensitivity for exchange names, or whether the output is sorted. This is acceptable for a filter tool but not fully complete.

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 has 0% description coverage, but the description compensates by explaining exchange examples, timeframe options, rating meanings (e.g., -3=Strong Sell), and a limit maximum. This adds significant meaning beyond the raw schema.

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 filters coins by Bollinger Band rating, using a specific verb and resource. However, it does not differentiate itself from the sibling tool 'bollinger_scan', which may have overlapping functionality.

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?

No explicit guidance is provided on when to use this tool versus alternatives like bollinger_scan or multi_timeframe_analysis. The description only lists parameters, leaving the context of appropriate usage implicit.

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