case_analytics

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 discloses that the tool operates on the entire database in real-time, mentions the massive dataset size (1.51亿 records as of 2026-07-22), and notes the previous documentation is obsolete. It also warns about dirty data in the year dimension. This is transparent and helps avoid misuse.

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 relatively long but every sentence provides critical information: real-time aggregation, dataset size, obsolete documentation, dimension details, filtering, and data caveats. It is front-loaded with the purpose and then dives into specifics. No wasted words.

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 complexity (no output schema, 5 parameters, real-time aggregation with large data), the description covers the tool's operation, input parameters, usage caveats, and distinguishes from siblings. It does not explain the exact output format beyond [{key,count}], but that is sufficient for an AI agent to use it correctly.

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 describes 3 of 5 parameters (dim, cause, province) with descriptions. The description adds context beyond the schema: it explains the meaning of each dim value, how to combine filters, and the importance of ignoring certain year values. This adds significant semantic value for an agent.

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's purpose: '案件大數據統計' (case big data statistics) performing real-time aggregation across the entire database. It specifies the available dimensions (cause, province, year, court) and that it returns aggregated counts. It distinguishes itself from sibling tools like case_stats, making the purpose unambiguous.

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 explicitly tells when to use this tool: it is the only tool that gives true full-database counts, and it contrasts with case_stats which provides sample counts from an 80,000 snapshot. It also warns about dirty buckets in the year dimension, guiding the agent to ignore them. This is excellent guidance.

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