Wizcom Limited

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

Annotations already establish that this is a safe, read-only, idempotent, closed-world operation. The description adds meaningful context beyond them: the scoring is based on 12 answers, returns a 0-100 score, a band, and prioritized gaps with fixes, mirrors a public web tool, and is educational only. It stops short of covering minor behavioral details such as whether inputs are validated or how ties are handled.

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 compact sentences that front-load the action, inputs, and output, followed by a necessary disclaimer. Every sentence 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?

There is no output schema, but the description explains what is returned: a 0-100 score, a band, and a prioritized gap list. All 12 inputs are fully documented in the schema, and the safety profile is covered by annotations. Nothing essential is missing for correct invocation.

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 every one of the 12 parameters already has a clear question and enum. The description adds only a summary of the topic areas and the yes/partly/no/unsure answer scale, without explaining the meaning of each parameter beyond the schema. Baseline 3 is appropriate when the schema carries the detail.

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 ('Score how ready') and resource ('cyber insurance application'), and enumerates the 12 assessment areas. An agent can immediately distinguish it from estimate_downtime_cost, get_wizcom_info, and scan_ai_visibility.

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 clear context that this is for small businesses preparing a cyber insurance application and sets the educational-use boundary. It does not explicitly name alternatives or exclusion cases, but the sibling tools serve unrelated purposes, so the intended use is unambiguous.

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, non-destructive), so the bar is lower, and the description still adds real value: it discloses the calculation basis (team size, salary, revenue), the three-way cost breakdown, and the source methodology at wizcomltd.com. The 'Estimate only' disclaimer manages expectations about precision, though it doesn't state units of the return values or any rounding 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?

Three sentences, purpose front-loaded, then return shape, then provenance and caveat — no filler. The middle sentence is dense but every clause (breakdown, three maintenance levels with hours/year) carries information the agent can't get elsewhere.

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, the description must explain the return value, and it does so concretely: hourly cost split into idle wages, lost revenue, and recovery, plus yearly cost at three named maintenance levels. Combined with rich annotations and fully documented parameters, an agent has everything needed to call and interpret this 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?

Schema coverage is 100%, so all three parameters already carry their own descriptions and min/max bounds. The description reinforces which inputs matter ('from team size, average salary, and annual revenue') but adds no syntax, unit, or edge-case detail beyond the schema — 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?

States a specific verb and resource — 'Estimate what one hour of IT downtime costs a business' — and immediately names the three inputs driving it. It is unmistakably distinct from siblings like check_cyber_insurance_readiness or scan_ai_visibility, so an agent can route without hesitation.

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 only implicit: the tool is clearly for producing a rough downtime-cost figure, and the 'Estimate only' caveat signals it is not a substitute for a real assessment. However, no alternatives are named and no when-not-to-use condition is given, leaving the agent to infer when this beats the sibling tools.

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 openWorldHint=false, so the safety profile is covered. The description adds useful context – output format is Markdown, content is sourced from wizcomltd.com, and slugs must match specific page slugs – but says nothing about caching, freshness, or failure behavior when a slug is invalid. With annotations carrying safety, this is a modest but real addition.

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?

One front-loaded sentence plus a compact topic inventory; every clause (Markdown format, source domain, topic meanings, slug requirement) earns its place with no 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 two-parameter, read-only content fetch with no output schema, this covers what the agent needs: what each topic returns, when a slug is required, and the output format. Only minor gaps remain (no output-schema, no error/invalid-slug behavior), which is acceptable given the annotations.

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 50%: the topic enum is documented in the schema, and slug has a description there too. The description adds meaning beyond that by tying slug to two different topic contexts and giving examples ('troy', 'macomb-county'), which the schema only partially covers for the location case.

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 ('Get information about Wizcom Limited ... as Markdown from wizcomltd.com') and enumerates exactly what each topic returns, so the agent knows precisely what it will get. Sibling tools are unrelated (insurance readiness, downtime cost, AI visibility), so no explicit differentiation is needed, but none is offered either.

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 topic list effectively tells the agent which topic to pick for which need (e.g. 'service (one service: pass slug)' vs 'services (list)'), and gives the exact condition for supplying a slug. It stops short of stating when not to use the tool or naming 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?

Annotations already declare readOnly, idempotent, non-destructive, open-world behavior, so the description correctly spends its words elsewhere. It adds two things annotations cannot: the result shape (score out of 5 with pass/fail per check) and a concrete rate limit of 20 scans per hour per visitor. It does not say what happens with an unreachable or malformed URL, which is the remaining gap.

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 sentences, no filler: the first carries purpose and the enumerated checks, the second carries the return shape and the quota constraint. Front-loaded with the verb and scope, with constraints placed last where they belong.

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 one-parameter read-only scan with no output schema, the description supplies the return format (score out of 5 plus per-check pass/fail) and the rate limit, which is what an agent needs to plan calls. Only the error/edge-case behavior for invalid or unreachable URLs is unaddressed.

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 url parameter already documents both the bare-domain and full-URL forms with an example. The description adds no syntax, normalization, or constraint detail beyond the schema, so the 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 verb and resource ('Run a free 5-check scan of a website's AI/agent-readiness basics') and enumerates exactly what is inspected: robots.txt, sitemap.xml, llms.txt, .well-known/security.txt, homepage JSON-LD. The sibling tools are unrelated audit/utility tools, so no disambiguation is needed and none is missing.

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 scope ('AI/agent-readiness basics') and the 'free' qualifier make the appropriate context clear, and the 5-check list implies this is a shallow baseline check rather than a deep audit. It does not state any exclusions or point to a deeper alternative, which keeps it just 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.