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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?

With no annotations, the description carries the full burden and discloses nothing: no return shape, no statefulness/session behavior, no indication whether it must precede checker_answer or checker_tree. The agent learns only a marketing-style subtitle.

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?

Extremely short and front-loaded, but that shortness is under-specification rather than efficiency — the single sentence duplicates the title instead of earning its place with actionable detail.

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 parameters and no output schema mean the description is the only source of behavioral information, and it supplies essentially none — an agent cannot tell what calling this returns or how it relates to the sibling flow tools.

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 there is no parameter semantics to explain; the baseline of 4 applies. Nothing in the description misleads about inputs.

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 restates the title almost verbatim ('The first question of the Council finder by postcode: which council handles it?') without saying what the tool actually does — whether it returns a question, begins a session, or performs a lookup. 'decision guide' is a label, not a verb+resource.

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?

No indication of when to call this versus siblings like checker_answer or checker_tree. The phrase 'first question' weakly implies it is the entry point of a flow, but that is inference rather than stated 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?

No annotations are provided, so the description carries the full disclosure burden. It communicates the tool's content (questions, options, verdicts) but says nothing about return format, payload size, or staleness of the tree. For a zero-parameter read-only lookup these gaps are tolerable but not fully addressed.

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?

It is a single sentence with no filler, and the resource is front-loaded. However, the colon-and-question construction ('which council handles it? guide') makes the sentence read awkwardly and forces a second pass to parse.

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, zero parameters and no annotations, the description needs to stand on its own. It conveys what the tree contains and why to fetch it, but leaves out how the result is structured, which matters for an agent planning to reason over it end to end.

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 baseline is 4; there are no inputs to explain. The description correctly implies no arguments are needed by presenting the tool as a dump of the entire tree.

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 precisely: the full decision tree of a Council finder by postcode, including every question, option and verdict. The word 'Every' and 'whole' distinguish it from checker_answer and checker_start, which by implication return a single answer or an entry point. The phrasing is slightly garbled ('handles it? guide'), but the resource is unmistakable.

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?

'For reasoning end to end' implies the tool is for cases where the agent needs the complete branching logic rather than a one-off answer, which hints at when to prefer it over checker_answer. However, it never names the alternative tools or states an explicit exclusion, so the agent must infer the choice.

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 behavioral burden. It discloses that nothing is bought, ordered, or paid for, that no quote is guaranteed, and that it is free. The word 'states' also conveys that this is an informational/read-only tool rather than one that submits anything.

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 front-loaded with 'Read first' and the core purpose, then adds expectation-setting and return details. It is slightly redundant with the title ('not a purchase, not a guaranteed quote' vs 'Nothing is bought, ordered or paid; no quote is guaranteed'), but each sentence still contributes useful 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 zero-parameter tool with no output schema, the description covers what the tool does, what the user should expect, and what information it returns. It could more explicitly mention alternatives like enquiry_fields or checker tools, but the 'Read first' instruction and submit_enquiry reference make the intended context sufficiently clear.

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 input schema is empty with zero parameters, so the baseline is 4. There are no parameters for the description to explain, and it appropriately focuses on what the tool returns 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 uses a specific verb and resource: it 'states plainly what submit_enquiry does' and spells out the returned content (who receives details, consent wording, confirmation method). This clearly separates it from the sibling submit_enquiry, which actually performs the submission.

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?

'Read first' explicitly positions this as the upfront explanatory tool before an enquiry is submitted. It doesn't exhaustively list when-not-to-use alternatives, but the direct reference to submit_enquiry and the 'Read first' instruction provide clear context for when to call 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 specifies exactly what information the tool exposes and notes that allowed options are included only 'where there are any', which adds useful nuance. It does not state return formatting or error behavior, but for a metadata/read-style tool this is reasonably transparent.

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 compact and front-loaded: the first sentence states exactly what the tool returns, and the second sentence explains the practical next step. No words are wasted, and the structure makes the tool's purpose immediately discoverable.

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-parameter metadata tool, this is nearly complete: it describes the returned fields, the domain, and the relationship to submit_enquiry. It does not explicitly state that the tool is read-only or describe the output envelope, but there is no output schema and the tool's nature makes these omissions minor.

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 provides no parameter-level meaning. The description nonetheless orients the agent by explaining the concept of 'field key' and how it should be used with submit_enquiry, which is helpful in a parameter-free context.

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 tool as exposing the field definitions of the Postcode Council enquiry (key, label, type, required, help text, allowed options). It lacks an explicit verb like 'list' or 'get', but 'Every field of the Postcode Council enquiry' makes the resource and scope unambiguous and distinguishes it from submission-oriented siblings like submit_enquiry.

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 gives practical guidance: use this tool to discover field keys, then pass answers to submit_enquiry keyed by those field keys. This implies a clear usage context, although it does not explicitly enumerate when not to use this tool or name other sibling alternatives such as enquiry_describe.

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 present, the description carries the full disclosure burden and meets it well. It reveals that Step 1 is validation-only, Step 2 actually submits the enquiry and triggers an email with a link that the person must click before providers see anything, and it quotes the exact required consent text. This goes far beyond what a schema typically conveys.

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, using 'Step 1' and 'Step 2' to convey a sequential flow without wasted words. The title reinforces the key caveats ('two steps; not a purchase'), and every sentence in the description earns its place by adding necessary behavioral or sequencing detail.

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?

There is no output schema, so the description appropriately explains what Step 1 returns (summary, consent line, confirmation token) and what happens after Step 2 (submission, email link, provider visibility only after link click). It also ties into enquiry_fields for answer keying, giving an agent the full call sequence needed to use the tool correctly.

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 each parameter at a basic level. The description adds meaningful orchestration context: answers must be keyed by field keys from enquiry_fields, confirmation is the token returned in Step 1 and used only in Step 2, and consent must remain true only after the person approves the summary. This enriches the parameters beyond their schema definitions.

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 opens with a specific verb and resource: 'Submits an enquiry to Postcode Council' and immediately distinguishes the tool from a purchase or guaranteed quote. It then explains the two-step nature, making the tool's purpose and scope unambiguous even without opening the 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?

The description gives explicit when-to-call instructions: Step 1 uses answers and consent=true to validate and get a token; Step 2 is only invoked 'if the person agrees' with the same answers, consent, and token. It also states what the tool is not for ('NOT a purchase, NOT a guaranteed quote'), providing clear usage boundaries.

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