VoiceDesk AI

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

Annotations already declare readOnly, idempotent, closed-world and non-destructive, so the safety profile is covered. The description adds that the result includes the smallest suitable plan derived from expected minutes, but says nothing about what an incompatible result looks like (empty result vs error) or how partial inputs 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?

A single dense sentence with the primary action front-loaded and the outcome clause at the end; every clause maps to a distinct input or output dimension 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?

With no output schema, the description does hint at the return (smallest suitable plan), but leaves format questions open and does not compensate for the 0% schema coverage on the string parameters. For a four-parameter tool with no parameter docs, a bit more format guidance would be expected.

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%, so the description must carry all four parameters, and it does name each (country, industry template, required capabilities, monthly minutes). However it omits format constraints the schema enforces silently: country is a 2-character code and industry must match a template name, both of which an agent could easily get wrong.

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 ('Check whether VoiceDesk AI fits a business') and enumerates the four fit dimensions plus the plan-sizing outcome. It is clearly distinct from create_demo_request/get_demo_status, though the overlap with get_pricing (which plans are suitable) is not addressed.

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 a concrete prerequisite: capability ids must come from get_services, which routes the agent correctly. It does not say when to prefer this over get_pricing or what to do if the agent lacks country/industry data, so no explicit exclusions are offered.

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?

Beyond the annotations (openWorldHint, destructiveHint=false), the description discloses that an external party will actually contact a US business, that no account is created and no charge occurs, and that the returned status_token is one-time. It also converts idempotentHint into an actionable instruction, which is genuine added context rather than a restatement.

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?

Five short sentences, front-loaded with the action and the two intent values, with no filler. The consent and no-charge/no-account clauses are compact and each adds 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?

For a write-only lead-capture tool with no output schema, the definition covers side effects (external contact, no account, no charge), safety (consent, idempotency) and the return values (tracking reference, one-time status_token). It stops short of naming get_demo_status as the consumer of the status_token or clarifying the conditional partner_type parameter, which an 11-parameter tool would benefit from.

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% across 11 parameters, so the description must carry the burden. It only explains the 'interest' enum semantics (demo vs partnership), implies 'consent_to_contact' via the consent sentence, and covers 'idempotency_key'; the other eight parameters (contact_name, business_name, phone, email, message, industry, best_time, partner_type) get no meaning, including whether partner_type is required when interest=partnership.

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 a specific verb ('Ask VoiceDesk AI to contact') and resource (a US business) and disambiguates the two intent modes by mapping each to its enum value ('interest=receptionist_demo', 'interest=partnership'). It does not distinguish this from sibling tools like create_onboarding_request or check_compatibility, so an agent still has to infer which request tool to pick.

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 prerequisite 'Only with the owner's consent' is stated and the retry guidance ('Send an idempotency_key to make retries safe') is actionable. However, there is no explicit comparison against create_onboarding_request or guidance on when a partnership inquiry should go here versus elsewhere; usage is implied rather than routed.

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?

Adds substantial context beyond the annotations: it explicitly says no account, subscription, phone number, or charge is created, which resolves the real risk of a mutation tool that is non-destructive but still has external effects. It also discloses the return values (tracking reference and a one-time status_token), which the annotations cannot 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?

Four short sentences, front-loaded with the core action and scope, followed by the outcome and the explicit non-effects. Nothing is redundant or padded.

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?

Covers the outcome and side-effect story, and though there is no output schema, it names the return values. However, for a 12-parameter mutation tool with 0% schema coverage, the absence of any parameter guidance leaves a material hole an agent would have to fill by inference.

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% across 12 parameters, so the description carries the full burden and largely fails. Only consent ('with its owner's consent') maps to a field; plan_code, idempotency_key, expected_monthly_minutes, timezone, industry, and website are never explained, leaving the agent to guess at their semantics.

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: prepare an onboarding request for a US business wanting VoiceDesk AI. It is clearly distinguishable from the demo-oriented siblings (create_demo_request/get_demo_status) by framing this as a pre-signup lead, though it never names those siblings 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?

Implies the use context well: the owner must consent, VoiceDesk reviews and sends a signup link, and the owner picks and pays for a plan themselves. That tells the agent this is a pre-sales handoff, not provisioning. It stops short of explicit when-not or named-alternative guidance against 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=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds genuinely new context: the status_token is a credential-like value issued at creation time, which tells the agent it must have been retained from the create response.

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 sentence, front-loaded with the object of the call. The enumeration of request types is slightly wordy but each item is load-bearing since it defines the supported scope.

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?

A simple two-parameter read tool with rich read-only annotations and no output schema; the description covers the credential requirement, which is the main risk of misuse. It doesn't hint at what status values can come back, a minor gap given there is no output 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 0%, so the description must carry the parameter burden. It explains where status_token comes from, which is the non-obvious part, but it never mentions that reference must match the VD- pattern or that the token is length-bounded – details an agent could only get by reading the raw 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 a specific verb and resource: retrieve the current status of a demo, partnership, or onboarding request. The three request types are enumerated, which separates it from create_demo_request and create_onboarding_request, though it never names those siblings 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?

Usage is implied by 'the status_token returned when it was created' – the agent can infer it is used after a create_* call – but there is no explicit when-to-use statement, no guidance on what to do if the token is lost, and no mention of 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 and closed-world, so the bar is lower; the description still adds value by naming the currency (USD) and the source of truth ('live plan catalog'), plus redirecting purchase intent off-tool. It doesn't say whether prices/plans can change between calls or how stale the catalog might be.

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 tight sentence (plus one clause) that front-loads the resource, currency, and returned fields. No filler or restatement of the 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?

With no output schema, the description usefully enumerates the return payload (monthly price, one-time setup fee, included minutes). It could be slightly stronger by stating whether the list is stable or frequently updated, but for a zero-arg read tool this is close to complete.

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 is nothing for the description to disambiguate. The description correctly implies the call is argument-free and unconditional.

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 resource and scope: current monthly plans from VoiceDesk AI's live plan catalog, in USD, with three enumerated returned fields. It is clearly distinguishable from get_services by the pricing/plan framing, but never names that sibling 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?

The note that 'purchases happen only on voicedeskai.org by the business owner' sets one useful boundary (this tool is informational, not a purchase path). However, there is no guidance on when to prefer this over get_services or how it fits into an onboarding flow.

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, closed-world, and non-destructive, so the safety profile is fully covered. The description adds genuine value by disclosing the content of the response, including the three-state capability model and the 'what agents may and may not do' policy scope, which matters given there is no output 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?

One sentence, front-loaded with the tool's subject and enumerating what it covers. Slightly list-like phrasing ('What VoiceDesk AI does: ...') but no wasted text.

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 input parameters and no output schema, the description's job is to tell the agent what it will learn, and it does so across four topical areas. A brief note on when to prefer the more specific siblings (pricing, compatibility) would make it fully complete.

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 the baseline of 4 applies. There is nothing for the description to compensate for on this dimension.

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 specifies the resource and enumerates the information returned: capabilities with availability states, supported industries, onboarding process, and agent permission boundaries. That is far more specific than the title alone. It doesn't contrast itself against siblings like get_pricing or check_compatibility, which keeps it from a 5.

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 implied — the agent is left to infer this is the general orientation/overview tool to call before the more specific siblings. There is no explicit 'use this when...' or statement that pricing questions should route to get_pricing or compatibility questions to check_compatibility.

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