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

No annotations are provided, so the description carries the full disclosure burden. It usefully reveals the score gate behavior ('<0.6 must re-verify before writing') and the scoping constraints, which is genuine behavioral value. But it never states whether the call mutates state or is a safe read, nor what it returns, leaving important behavior inferred rather than stated.

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?

Two tightly packed sentences: the purpose is front-loaded, then the hard constraints follow in a labeled clause. Very little waste, though the compressed constraint block assumes prior familiarity with the surrounding workflow vocabulary.

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?

With no annotations, no output schema, and 7 params at 0% schema coverage, the description should carry heavy load. It covers the operationally critical constraints well but omits query/domain/run_id semantics and any hint of the response shape, leaving the definition only partially complete for a tool this parameterized.

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 description coverage is 0%, so the description must compensate. It does explain bucket (bucket=core), entries (explicit entries), keywords (≤ 8), and the supplementary/external list (≤ 10, must include url). But query, domain, and run_id are left entirely undocumented, so only part of the 7-parameter surface gains meaning.

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 names a specific operation (发布前自检 = pre-publication self-check) and enumerates its two outputs: relevance/directional coverage and scoring of supplementary entries. It clearly reads as a measurement/validation tool, but it never distinguishes itself from the sibling tools radar_scan or radar_pack, so an agent cannot tell them apart without external context.

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 gives concrete usage context (pre-publication) and explicit exclusions such as 'measure the entries about to be published (bucket=core or explicit entries), do not pass the whole pool'. These are real when/when-not constraints. However, it does not name an alternative tool or state which situations should route to radar_scan/radar_pack instead, so it stops short of full routing guidance.

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

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

With no annotations, the description carries the full burden. It usefully discloses that the default return is only scores+flags and that summary text requires explicitly setting fields=['summary'], which is a real calling gotcha. However, the stated caps (ids ≤ 12, limit ≤ 20, 240-char summary) merely restate schema limits, and it never states the read-only nature, pagination/offset behavior, or what 'digest' refers to.

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 dense clauses with zero filler: purpose first, then usage condition, then hard constraints. Every sentence is front-loaded and carries information relevant to invocation.

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 7-parameter tool with no annotations, no output schema, and 14% schema coverage, the description covers purpose, usage, and the key default-fields behavior but leaves several parameters and the response structure under-specified. Adequate to call it, but not 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?

Schema description coverage is only 14%, so the description must compensate, and it partially does by explaining ids/limit/fields/summary_chars constraints and the id-vs-bucket selection modes. But it leaves bucket semantics, offset, and run_id unexplained, and most of what it says about ids/limit duplicates the schema's own maxItems/maximum values.

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 states a specific verb and resource: fetch body text and details (取正文与细节) by id or bucket. This is clearly distinct from a scan/check operation, but it never names the siblings radar_scan or radar_check, and instead references a 'digest' that is not a listed tool, so sibling differentiation is only implied.

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 gives an explicit usage condition: use only when the digest is not enough for writing (只在 digest 不够写的时候用). This tells the agent when to reach for this tool versus staying with summary-level output, though it never names the actual alternative tools (radar_scan / radar_check), leaving routing partly to inference.

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

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

With no annotations the description must carry the burden, and it does: it discloses engine-computed truth/comm with a 0.6 gate, the 0.55–0.65 borderline flag requiring review, and that the calibration bucket is never a content source. These are real behavioral constraints an agent cannot get elsewhere. Minor gaps around performance/cost are not covered.

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?

Front-loads the core pipeline and output promise, then layers scope logic and hard constraints in short, dense lines with no filler sentences. Tightly organized despite the volume of caveats.

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?

No output schema exists, so the description must describe returns, and it does: digest fields (id/标题/truth·comm/来源/日期/new), inline summary depth, and scope_source. Combined with the constraint notes this is nearly complete for a 13-param orchestration tool, with only peripheral params left undocumented.

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 54% across 13 params. The description adds meaning for domain (auto/explicit/broad fallback), briefs, and major_days ('in_window=false 需写明日期'), but leaves top, days, streams, brief_chars, force_refresh, and max_per_source to the schema. Partial compensation, so baseline 3.

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?

States a specific three-step pipeline (采集 + 评分 + 分桶) and its output (可写简报的材料), so the verb+resource is unambiguous. It explicitly distinguishes itself from radar_pack ('要更多正文再用 radar_pack'), but the third sibling radar_check is never mentioned. Strong, though not fully sibling-differentiated.

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?

Gives a concrete default-use rule ('多数情况下一次调用就够写') and names the alternative (radar_pack) plus the condition that selects it (need more body text). Also explains scope auto-detection behavior to guide invocation. No explicit when-not-to-use against radar_check, so not a 5.

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