filter_statistics

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

Describes that data_id reads stored data without including it in context, and returns a JSON string (with optional summary). No annotations exist, so description carries the burden; it adequately discloses the tool's read-only, filtering nature.

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?

Well-structured with Args section and example. Slightly verbose but each sentence adds value. Could be more concise, but effective.

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?

Covers both data sources, all parameters, return format, and an example. Missing details on error handling or edge cases, but sufficient for a filtering tool with output schema.

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?

With 0% schema description coverage, the description fully compensates by explaining each parameter: regions, periods, items, format (enumeration of 'summary'/'raw'), data_id (source), and data_json (alternative source). Examples clarify usage.

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 statistics data and explains two modes (data_id vs data_json). It does not explicitly differentiate from sibling tools like search_statistics or aggregate_statistics, but the purpose is clear.

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?

Provides guidance on when to use data_id (efficient, avoids LLM context) vs data_json. Includes an example. However, lacks comparison with alternative sibling tools for filtering or searching.

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