query_logs

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

With zero annotations, the description carries the full burden and largely delivers: it discloses hard caps on rows/line length/total response, indicates truncation markers ('超出会标注省略'), and reveals the platform's protective limits. Slight gap: doesn't explicitly state read-only safety, rate limits, or failure behavior.

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?

Three sentences, each earning its place: purpose, volume-control strategy, and when-to-use vs alternative. Front-loaded and telegraphic, no fluff. The semicolon-delimited hard-cap list packs maximum information density per character.

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?

For a high-complexity tool (13 params, no output schema, no annotations), the description covers the critical operational concern (data volume) thoroughly, including defaults and caps, and points to how to avoid hitting limits. Could add error/auth comments or output format hints, but the volume story—the main pitfall for log queries—is well told.

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?

Schema coverage is 92%, so per rubric the baseline is 3 and the schema already documents per-parameter meaning well (e.g., requestId 'strongly recommended', range default, maxPages formula). The description adds cross-parameter strategy (combine requestId + narrow time window) which is genuinely useful but mostly reinforces rather than extends schema semantics.

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?

Starts with a specific verb+resource ('Query raw logs') and immediately distinguishes scope. The closing sentence explicitly contrasts with diagnose_issue ('automatic aggregate analysis' vs 'precise evidence following clues'), which disambiguates from siblings and reinforces purpose.

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?

Explicitly names diagnose_issue as the preferred tool for production troubleshooting, and positions this tool ('suitable for...') for clue-based evidence collection. Also gives concrete operational guidance (use requestId, pair with narrow time windows, default 15m) that tells the agent when and how to invoke correctly.

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