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

With no annotations, the description carries the full burden. It does disclose the key behavior—returning either the next question or a final verdict—but it does not mention whether the flow state is mutated, what happens for invalid choices, or any session/state requirements.

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, information-dense sentence. The title and description work together without repetition or filler, and the key input/output behavior is front-loaded.

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?

Adequate for a two-parameter tool: it names both inputs and describes the possible return. However, with no output schema and no annotations, it falls short of fully describing the response shape, error behavior, or what the caller should do with the returned next question/final verdict.

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 schema has 0% description coverage, so the description's clarification that `question` is a question id and `choice` is the chosen option's index is essential. It adds meaning beyond the raw property names and types, though it omits details like how to obtain valid question ids or bounds beyond minimum 0.

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 operation: given a question id and a choice index, return the next question or final verdict. This clearly differentiates it from sibling tools like checker_start or checker_tree, which have different roles in the flow.

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: use this when a user has answered a question and the flow should advance. However, it does not explicitly state when not to use it, what prerequisites exist (e.g., starting with checker_start), or how it relates to alternatives.

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 provided, so the description carries the full burden of explaining behavior. It only states that the tool is the first question of a decision guide; it does not say what happens when invoked, whether it returns a question, whether it creates state, or how the agent should proceed after calling it.

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 brief and free of fluff, but it is under-specified rather than efficiently concise. A single noun phrase leaves the agent without enough actionable structure about what the tool does or returns.

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 output schema and no annotation context, the description should explain what the agent can expect from calling this tool and how it fits into the decision guide flow. It only labels the tool as the first question, leaving critical operational context 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 has zero parameters and the input schema is empty, so there are no parameter semantics for the description to clarify. A baseline of 4 is appropriate because no parameter documentation burden exists.

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 trial route fits?' decision guide, so an agent can infer it is the entry point. However, it uses a noun phrase rather than a specific verb like 'starts' or 'returns', and it does not clearly distinguish the tool's function from checker_answer or checker_tree beyond its name.

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 guidance on when to call this tool versus alternatives such as checker_answer or checker_tree. The word 'first' implies usage at the beginning of the guide, but the description does not state prerequisites, next steps, or conditions for choosing a sibling tool.

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 transparency burden. It discloses the content returned—every question, option, and verdict—but does not describe the return format, size, or whether the tool is purely read-only. Since it is a zero-parameter informational tool, this is a moderate gap rather than a 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?

The description is a single compact sentence that front-loads the essential scope: 'Every question, option and verdict.' Each word earns its place, with no fluff, repetition, or unnecessary context.

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 no-input tool, the description provides the core content scope and the intended use case ('reasoning end to end'). The sibling names help position it as the full-tree counterpart to more specific checker tools. It stops short of describing output structure or explicitly contrasting with siblings, but this is acceptable for this simple tool.

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 the input schema is empty, so schema description coverage is effectively 100%. There is nothing for the description to add about parameter meaning. The baseline of 4 for no-parameter tools 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 identifies the resource—the complete 'Which trial route fits?' decision tree—and enumerates its contents: every question, option, and verdict. It does not use a strong action verb like 'retrieve' or 'list', nor does it name a sibling directly, but 'whole decision tree' clearly contrasts with node-level tools like checker_answer and checker_start.

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 'for reasoning end to end' gives an implied use case: call this when you want the full guide in a single view. However, it never explicitly says when to use it instead of checker_answer or checker_start, and it offers no exclusions or alternative routing. Guidance is present but only 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?

With no annotations, the description carries the full burden of behavioral disclosure. It explains that the underlying enquiry is not a purchase, no quote is guaranteed, and it is free, and it details what the describe tool returns. This goes beyond a generic 'describes submit_enquiry' statement.

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 three sentences, each earning its place: a directive to read first, a plain explanation of what the action does, and a summary of the returned information. It is front-loaded and free of filler.

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, no-output-schema tool, the description is complete. It tells the agent what the tool is for, clarifies important caveats about the underlying enquiry, and lists the specific information it returns.

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 parameter semantics are not applicable. Per the baseline for zero-parameter tools, this is a solid score; the description focuses on behavior and return value instead.

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 a specific purpose: it explains what submit_enquiry does on Football Trials Database. The title and 'Read first' framing make it distinct from the sibling action tools, particularly submit_enquiry itself.

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 instruction 'Read first' explicitly signals when to use this tool: before using submit_enquiry. It gives clear context about the enquiry process, although it does not explicitly name alternatives 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?

With no annotations provided, the description carries the burden of behavioral disclosure. It communicates that the tool exposes all enquiry fields and their metadata, implying a read-only metadata lookup. It does not discuss side effects, authentication, or whether the data is static or dynamically generated, but for a simple field-listing tool the description is not misleading.

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 two sentences with no filler. The first sentence front-loads the core content of the tool, and the second sentence adds a clear, actionable relationship to submit_enquiry. 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?

For a zero-parameter metadata tool with no output schema, the description explains the return content well and connects it to the submission workflow. It could be slightly more explicit about the output shape, but the enumerated fields give an agent enough understanding to use the tool and interpret results.

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 the schema coverage is 100% by virtue of an empty schema, so the baseline is 4. The description adds useful semantic context by explaining what each field represents and how field keys are used downstream in submit_enquiry, which is valuable even though there are no parameters to document.

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 resource (the Football Trials Database enquiry fields) and enumerates exactly what is included: key, label, type, required flag, help text, and allowed options. It distinguishes itself from submit_enquiry by explaining that answers must be keyed by these fields, though it lacks an explicit verb like 'retrieve' or 'list'.

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 implies when to use the tool: when you need the field definitions to construct a submission, since it says to pass answers to submit_enquiry keyed by field key. However, it does not explicitly compare against siblings like checker_answer, checker_start, or enquiry_describe, nor state 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?

With no annotations, the description carries the full behavioral burden and does so richly: it discloses that this is not a purchase or guaranteed quote, that step 1 only validates and returns a summary/consent line/token, that submission is gated on human approval, and that the person must click an emailed link before any provider sees the data. It even enumerates what the consent actually authorizes (up to three academies/agents, notification per firm, ability to stop).

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 not-a-purchase framing, then the two steps in order — a good structure. It is somewhat long, and the full consent sentence is quoted verbatim in the description while the schema's consent field repeats the same text, a minor redundancy that costs a point.

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 annotations and no output schema exist, yet the description still specifies the step-1 return values (summary, consent line, confirmation token), the required human-in-the-loop checkpoint, and the post-submission email verification flow. Nothing an agent needs to call this correctly across two turns 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 coverage is 100%, so the baseline is 3, but the description adds sequencing semantics the schema can't express: "answers" come keyed by field keys from enquiry_fields, the same "answers" plus consent=true are re-sent in step 2, and "confirmation" is the token from step 1 used only after the summary is approved. This goes beyond the per-field descriptions.

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 verb and resource ("Submits an enquiry to Football Trials Database") and immediately disambiguates with two negations: "NOT a purchase, NOT a guaranteed quote." It also names the sibling that supplies its input data ("keyed by field key from enquiry_fields"), so an agent can distinguish it from enquiry_fields/enquiry_describe without opening a schema.

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 an explicit two-step protocol: Step 1 with answers + consent=true to validate, Step 2 "only if the person agrees" with the same answers plus the confirmation token. It also states the precondition for consent (the person has read and agreed to the quoted text), so both when-to-call and when-not-to-proceed are covered.

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