site

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It reveals the return shape (next question or verdict) but does not disclose whether submitting an answer mutates or advances the flow state, whether it is reversible, or what happens on invalid question ids or out-of-range choice indices.

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 front-loaded sentence that states inputs first ('Given a question id and the chosen option') then output ('return the next question or the final verdict'). There is zero filler and every word earns its place.

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 and no output schema, the description is thin for a tool embedded in a multi-step workflow. It omits where question ids originate, how options map to choice indices, whether answers are permanently recorded, and error behavior—covering only the bare input/output contract.

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%, and the description compensates by giving meaning to both parameters: 'question' is labeled a question id, and 'choice' is clarified as the chosen option's index. This adds semantics beyond the bare string/integer schema, though it does not state where the option indices come from.

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 resource: given a question id and a chosen option index, return the next question or final verdict. This clearly identifies the tool's function, and the name 'checker_answer' together with the action distinguishes it from checker_start at a glance, though it never explicitly names or contrasts with 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?

Usage context is implied: the title 'Answer a question, get the next step' suggests this is the step taken after receiving a question in the checker flow. However, there is no explicit when-to-use guidance, no mention of alternatives like checker_tree, and no exclusions or prerequisites.

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 and no output schema, the description carries the full burden of behavioral disclosure, but it only states what the tool is, not what happens when called. It does not say whether the tool returns a question, prompts the user, or has side effects, leaving the agent to infer the tool's runtime 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?

The description is a single, front-loaded sentence with no redundant wording. It immediately establishes the tool's position in the decision guide without wasting tokens.

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 description is too sparse for an agent to know how to use the tool correctly in context. It does not explain what the returned question looks like, what the agent should do with the response, or how this connects to checker_answer and checker_tree, and there is no output schema or annotation to fill that 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?

The tool has zero parameters, so there is nothing for the description to explain about parameter usage. Baseline 4 is appropriate because no parameter documentation is needed.

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 identifies the tool as 'The first question of the Which PDF to CSV route? decision guide,' which clearly establishes its role as the entry point of a specific decision flow. It is distinguishable from siblings like checker_answer and checker_tree by the 'first question' framing, though it lacks an explicit verb describing what invoking the tool actually does.

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 conveys clear context that this tool is used at the beginning of the decision guide, since it is explicitly the 'first question.' It does not mention exclusions or alternative tools, but the 'first' positioning implicitly tells the agent to start here before using siblings like checker_answer.

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?

No annotations are present, so the description carries the transparency burden. It discloses the content scope and reasoning purpose, which implies a read-only, informational operation, but it does not state return format, size, or any side-effect/safety profile. For a parameterless content-retrieval tool this is adequate but not rich.

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 compact sentence that front-loads the core content scope (every question, option, and verdict) and adds a concise purpose clause. No filler or repetition of the tool name/title.

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 parameterless, static decision-tree retrieval, the description tells the agent what it will get and why it is useful. It could be more complete by noting output format or that checker_answer/checker_start cover focused steps, but nothing essential for a correct call 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 the schema is complete and there are no parameter semantics to document. The description correctly focuses on what the returned tree contains rather than input details, matching the baseline for parameterless tools.

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 identifies the resource as the complete decision tree for the 'Which PDF to CSV route?' guide, enumerating questions, options, and verdicts. It implies a retrieval use but lacks an explicit verb such as 'returns', and only weakly differentiates itself from the checker_answer/checker_start 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?

Usage is implied by 'for reasoning end to end': an agent can infer this tool is for the whole guide rather than a single step. There is no explicit when-to-use guidance, no exclusion of the sibling tools, and no note about preferring checker_start/checker_answer for focused queries.

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 provided, the description carries the full burden of disclosure. It explicitly states that nothing is bought, ordered, or paid, that no quote is guaranteed, and that the process is free, while also describing the return content (recipients, consent wording, confirmation). This gives the agent a clear model of side effects and expectations with no contradiction.

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 are front-loaded with 'Read first' and every sentence adds distinct value: purpose, constraints, and return content. There is no repetition or filler, making it an efficient and well-structured description.

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 zero parameters, no annotations, and no output schema, the description provides the essential context: what the tool does, what it guarantees not to do, and the specific information it returns. It would be slightly stronger with an explicit note on the output format, but it is sufficient for an agent to decide when and how to use it.

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 has zero parameters, so the schema is empty and the baseline is 4. There is nothing to add about parameter meaning; the description appropriately focuses on the tool's output and the context of submit_enquiry.

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 purpose: 'States plainly what submit_enquiry does on CSV Convert', clearly a verb+resource action. It also distinguishes itself from the sibling submit_enquiry by being the 'read first' explainer, and the title reinforces that it describes an enquiry with a human, not a purchase or guaranteed quote.

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 opens with 'Read first', giving a clear directive to use this tool before submit_enquiry, and explains what the tool will tell the agent. However, it does not explicitly mention when not to use this tool or compare it to the checker_* sibling tools, so it falls short of full alternative 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 provided, the description carries the full burden of behavioral disclosure. It does explain that the tool returns all enquiry fields and what those fields contain, which makes the read-oriented behavior fairly transparent. However, it does not explicitly state that the tool has no side effects or describe the exact response structure, leaving some ambiguity about the return format.

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 a single dense sentence that front-loads the core purpose and then gives a practical downstream usage hint. Every part earns its place, with no filler or redundant restatement of the tool name or title.

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-parameter metadata-listing tool without an output schema, the description is complete: it enumerates the field metadata returned and tells the agent how to use those values. Nothing critical is missing for an agent to understand and invoke the tool correctly in the context of its siblings.

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 has zero parameters and an empty input schema, so there are no parameter semantics for the description to clarify. The schema description coverage is effectively complete because there is nothing to document. Under the zero-parameter baseline, the description adds appropriate context by explaining how the returned field keys should be used with submit_enquiry.

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: it returns every field of the CSV Convert enquiry, including key, label, type, required status, help text, and allowed options. The resource is specific and the description goes beyond a tautology. It does not explicitly distinguish itself from the sibling enquiry_describe, but the focus on 'every field' with metadata is sufficiently clear.

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 provides actionable usage guidance by telling the agent to pass answers to submit_enquiry keyed by the field keys returned here. This gives clear context for when to use the tool before submitting an enquiry. It does not explicitly list when not to use it or compare with alternatives, but the integration hint is strong enough for a 4.

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 provided, the description carries the full burden and excels: it discloses the two-step validation/submission flow, the return of a confirmation token, the need for user approval before Step 2, and the email-link requirement before providers see the enquiry. It also defines consent exactly, which is critical for correct invocation.

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 dense but well-structured with explicit 'Step 1' and 'Step 2' labels. Every sentence adds necessary information for correct use, though slightly longer than average. It earns its length given the two-step complexity.

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 tool with no annotations and no output schema, the description provides nearly everything an agent needs: how to initiate, how to confirm, what to show the user, and what happens after submission. Minor gaps remain (e.g., behavior if consent is false, what the confirmation token looks like), but the core flow is fully covered.

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. The description adds real value beyond the schema by explaining the workflow roles of each parameter: answers keyed from enquiry_fields, consent='true', and the confirmation token from Step 1. This is more than a restatement of 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?

States the specific action ('Submits an enquiry to CSV Convert') and immediately clarifies what it is not ('NOT a purchase, NOT a guaranteed quote'). The two-step nature is front-loaded in both title and description, making it distinct from siblings like enquiry_describe and enquiry_fields.

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?

Provides clear step-by-step invocation guidance: Step 1 with answers/consent, then Step 2 with the confirmation token. It references enquiry_fields for keying answers and specifies the consent requirement. However, it does not explicitly name alternative tools or state when not to use this tool beyond the 'not a purchase' warning.

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