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

Annotations already declare readOnly/idempotent/openWorld=false, so the bar is low, and the description adds real context: answers are 'calibrated', a `contract` directive is returned, and the grade gates action. It also notes a one-forward-pass execution model, which the annotations do not convey.

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 short sentences, front-loaded with the core action, and each sentence carries information (what it does, what it returns, how to compose). No filler or restatement of the name.

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 an output schema present, return values need not be spelled out, yet the description still explains the `contract.grade` field's significance. Combined with 100% schema coverage on 8 parameters, the definition is nearly complete for correct invocation; only sibling routing is missing.

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 100%, so parameters are fully documented structurally, establishing a baseline of 3. The description adds one genuine semantic point – that a `preset` can be composed with inline `questions` in the same pass – but otherwise repeats the schema's job.

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 clear verb+resource ('Answer a typed question set about `state`') and clarifies the scope ('in one forward pass'). It is distinguishable from siblings like decide_yes_no and decide_shortlist by the word 'typed', but it never explicitly contrasts itself with them, so the differentiation is inferential rather than stated.

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 only usage direction is about consuming the result: 'act only when it is `auto_execute`'. That is useful downstream guidance, but there is no when-to-use-this-vs-a-sibling guidance despite three closely related decide_* tools existing. Usage is implied rather than stated.

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?

Annotations already declare readOnly, idempotent, and closed-world behavior, so the safety profile is covered. The description adds real value beyond that: the two-stage embedding/shortlist mechanics, the fixed token budget rationale, and the shortlisted:false no-op outcome for oversized k.

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-loaded with the core purpose, then the mechanism, then the usage threshold and edge case, with little waste. The line breaks and title repeat add minor friction but no meaningful bloat.

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 an output schema present (return values need not be explained) and annotations covering the safety profile, the description supplies the remaining essentials: mechanism, when-to-use threshold, and the no-op edge case. Naming the decide sibling directly would close the last gap.

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 100%, so the baseline is 3 and the schema already documents parameters. The description adds meaning for k (top-k kept from the shortlist stage, no-op above the label count) and ties threshold back to the decide tool, going beyond the schema text.

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 (pick one label from many) and frames the tool's distinctive mechanism (shortlist-then-decide) versus a plain decision pass. Sibling differentiation is implicit rather than explicit: it gestures at the 'normal decision pass' (i.e. decide) and a label-count threshold, but never names the alternative tool.

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 a concrete activation condition ('above ~40 labels, or whenever labels are long') plus an edge-case rule (k above the label count is a no-op returning shortlisted: false). It does not explicitly say which sibling to use below the threshold, so the routing is inferable but not spelled out.

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?

Annotations already cover the safety profile (readOnly, idempotent, closed-world), and the description adds genuinely new operational context: this is the cheapest call and marginal questions are nearly free once the model is loaded. That cost/latency framing is exactly the kind of trait annotations cannot express. It does not discuss caching behavior directly, though `use_cache` is documented in the schema.

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 short sentences, front-loaded with the tool's value proposition, then the phrasing rule, then the alternative. No filler, though the hard line breaks inside sentences make it slightly less scannable than it could be.

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?

An output schema exists, so return values need not be explained, and the description still previews the shape ('a calibrated yes/no and a directive'). For a five-parameter tool with full schema coverage and annotations, the remaining description content is sufficient; only the threshold semantics (deferred to `decide`) are left to the 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?

Schema description coverage is 100%, so the schema already documents all five parameters, including the meaning of `statement` and `state`. The description reinforces the phrasing convention for `statement` ('phrase positively, true = go ahead'), but that largely duplicates the schema's own wording, so baseline 3 is correct.

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 makes clear this tool evaluates a single statement and returns a calibrated yes/no plus a directive, and it explicitly contrasts that scope with the multi-question sibling `decide`. It stops short of a clean verb+resource phrasing (e.g. 'evaluate one statement'), but an agent can confidently distinguish it from the other siblings.

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 states the positive-phrasing convention and gives an explicit routing rule: 'Prefer `decide` when you have several questions, since extra questions cost almost nothing once the model is loaded.' That names the alternative and the condition that selects it, though it does not spell out when this tool should NOT be used.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=false, so the safety profile is covered. The description's 'Echo' wording is consistent with those annotations, but it adds no extra behavioral detail such as error behavior, auth needs, or rate limits.

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 short sentences with zero waste: the first states the purpose, the second states when to use it. Information is front-loaded and appropriately sized.

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 simple read-only expansion tool with rich annotations, full schema description coverage, and an output schema, the description covers purpose and usage sufficiently. It could be slightly more complete by clarifying how preset and inline questions relate or which one takes precedence.

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 100%, so the schema already documents both parameters ('Preset name' and 'Inline questions'). The description mentions output fields, not input semantics, and adds no clarification about how preset and questions interact; baseline 3 is appropriate.

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 ('Echo') and resource ('a question set'), and enumerates the expanded fields (ids, kinds, options, levels). It is clear what the tool does, though it does not explicitly distinguish itself from siblings like list_presets or decide.

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 a clear usage context: 'Call this when you need the exact option labels before deciding.' This implicitly positions it ahead of decision-making siblings, but it does not name an alternative tool or state when not to use it.

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?

Annotations already declare readOnlyHint, idempotentHint, and a closed-world scope, so the safety profile is covered. The description adds the useful linkage that values are passed as `preset`, but says nothing about the size or shape of the returned list beyond what the output schema presumably provides.

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?

A single sentence with no filler, front-loading the resource and its purpose. It is arguably too terse to be maximally useful, but nothing in it is wasteful.

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?

Because an output schema exists, the description need not explain return values. Still, for a discovery tool in a set of nine decision-related siblings, the description does not say enough to route the agent confidently here versus describe_questions.

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 tool takes no parameters, so there is nothing for the description to disambiguate; baseline 4 applies. No parameter meaning is missing.

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 the resource (curated question sets) and its role as a `preset` value, so an agent understands it enumerates usable presets. However it never states the verb 'list' explicitly and does not distinguish itself from the sibling `describe_questions`, which an agent could easily confuse it with.

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 phrase 'instead of writing your own' implies usage context – use this to discover presets rather than author your own question set. But there is no explicit when-to-use vs alternatives guidance, and no mention of how the returned presets feed into other tools such as decide.

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?

Annotations already declare readOnlyHint=false, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds genuinely useful behavioral context beyond that: it downloads hundreds of MB on first use and will be slow. It does not describe memory-residency duration or failure modes, so it stops short of a 5.

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 short sentences, zero waste. The core action is front-loaded, followed by the rarity caveat and the cost warning in descending order of importance.

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 zero-required-parameter warmup tool with an output schema and full annotation coverage, the description covers what it does, when to use it, and its performance cost. Nothing an agent needs to decide whether and how to invoke it is missing.

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 100% and the single optional 'models' parameter is documented in the schema with its valid checkpoint values. The description adds no syntax, default, or selection guidance beyond what the schema already provides, so the baseline of 3 applies.

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 action and effect: 'Force checkpoints into memory so later decisions are fast.' That is a clear verb+resource+outcome. It does not explicitly contrast with any sibling, but the siblings (decide, route_request, server_status) are functionally distinct enough that differentiation is not critical.

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 explicit when-not guidance ('Rarely needed: the HTTP server warms up at start') and a precise triggering condition for when to use it ('only if you connect over stdio or the server was started with warmup disabled'). This is exactly the when/when-not structure that earns a 5.

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?

Annotations already declare readOnly, idempotent, and closed-world behavior, so safety is covered. The description adds a genuinely useful performance trait -- 'microseconds of work, no model download or forward pass' -- that an agent cannot get from the annotations.

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 short sentences, front-loaded with the core action and then the cost/positioning rationale. No filler, though the final sentence could be tightened into the first.

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 an output schema present, return values need not be described, and annotations cover the safety profile. The description supplies purpose and cost context; only the meaning of the required 'state' payload is left entirely to the 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?

Schema description coverage is 100%, so the schema already documents lang ('force the language branch'), model, state, and questions. The description adds nothing about parameter syntax or interaction, which is acceptable at full coverage but not additive.

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: 'detect language/script and workflow, and name the checkpoint that fits.' The 'no model download or forward pass' framing implicitly separates it from the inference-based siblings (decide, decide_yes_no), though no sibling is named explicitly.

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 a positional hint -- 'useful before deciding whether a request needs the multilingual checkpoint at all' -- which implies this is a pre-flight step ahead of a decide-family call. However, no alternative tool is named and no when-not condition is given, so the routing guidance stays implicit.

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?

The annotations already declare readOnlyHint=true, idempotentHint=true and openWorldHint=false, so the safety and side-effect profile is fully covered by structured data. The description contributes only a list of data categories, which is largely redundant with the output schema and adds no fresh behavioral context such as freshness, cost, or anything about the readiness semantics.

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?

A single short phrase with no filler, and the most decision-relevant item (readiness) is front-loaded. It is slightly under-structured as a bare noun list with no verb, but nothing is wasted.

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 zero parameters, an output schema covering return values, and annotations covering safety, the only remaining burden on the description is context, which the short content list supplies adequately. It could say a little more about what 'readiness' means, but nothing essential to calling the tool is missing.

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 tool takes zero parameters, so per the rubric the baseline is 4. There is nothing for the description to disambiguate, and the empty schema is self-explanatory.

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 fragment enumerates exactly what the tool exposes (readiness, loaded checkpoints, cache size, latency percentiles), which is specific to a status/inspection resource and clearly separable from the decision-oriented siblings. It lacks an explicit verb like 'returns' or 'reports', so it reads as a content list rather than a stated action, but the resource is 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?

There is no when-to-use guidance, no statement of when an agent should check server status versus calling preload_warmup or the decide_* tools, and no prerequisites or exclusions. The agent must infer the usage context entirely from the tool name.

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?

Annotations already establish read-only, idempotent, closed-world behavior, so the description need not restate safety. It adds genuine behavioral detail the schema cannot: the internal composition of three questions and the reduction rule ('strictest verdict'), which tells the agent how the boolean is derived.

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 short sentences, front-loaded with the core answer and followed by mechanism and usage. No filler; each sentence carries distinct information.

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 full schema coverage, rich annotations, and an output schema, the description only needs to convey purpose, mechanism, and usage, all of which it does. The only soft spot is the unexplained `threshold` cross-reference, which is minor given the 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?

Schema description coverage is 100%, so both parameters are documented structurally, and an output schema exists. The description adds no syntax or semantics for `state` or `threshold` beyond a cross-reference ('As in `decide`'), so baseline 3 applies.

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 outcome ('one boolean answer: does a human need to see this?') that separates it from the decide_* siblings, which produce richer verdicts. It does not explicitly name which sibling to prefer, but the boolean framing is distinctive enough for selection.

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?

'Use it as a gate in front of an irreversible step' gives a clear trigger condition. No explicit when-not or named alternative (e.g., 'if you want the full rationale, use decide'), so it stops short of a 5.

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