count_bill

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

The description discloses the return format including estimated_rows, is_exact, and hint, and explains the implication of is_exact=false (actual rows >= estimated). It also states that no data content is returned, which is a key behavioral trait. Given no annotations, this covers the main behavioral aspects.

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 organized with a clear purpose, usage, return format, and attributes. Each section provides useful info without unnecessary fluff, though it is somewhat verbose for a simple count tool.

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?

The tool has 2 parameters (both explained), an output schema, and the description covers the return fields, the estimation caveat, and usage guidance. Considering the moderate complexity, the description is fully sufficient.

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 input schema provides no descriptions (coverage 0%), but the description gives concrete examples for both form_id (e.g., SAL_SaleOrder) and filter_string (e.g., FDate condition), adding essential meaning beyond the 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 it estimates the number of rows for a query condition (估算某查询条件下的数据行数) and explicitly notes it does not return data content (不返回数据内容), distinguishing it from sibling query tools.

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?

It explicitly states the intended use as probing before large data volume queries (用于大数据量查询前的探测) and advises monthly/weekly sharding when the estimate is not exact (建议按月/周分片查询). It does not name alternatives explicitly but provides clear context for when to use this tool.

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