REP27 — EU & UK legal representative

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 fully covered by structured data. The description adds only the scope of returned content, with no note on auth needs, rate limits, or how the answer is structured — acceptable given annotation coverage, but not additive.

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 with no filler; the content list does all the work. It is terse but every word earns its place, and nothing is buried.

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 zero parameters, an output schema carrying the return structure, and annotations covering the safety profile, the description only needs to state what domains the tool covers — which it does. It is complete for this low-complexity tool, though a hint about when to reach for it would round it out.

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 no parameter behavior for the description to clarify, and the schema is trivially complete.

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 enumerates the specific content domains the tool returns — provider identity, legal entities, contacts, terms, and verification — which lets an agent distinguish it from the transactional siblings (create_checkout, get_plans, check_obligations). The verb is implicit ('About'), but the listed resources make the purpose unambiguous.

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: this is the informational lookup to call when the agent needs service/provider metadata. However, the description gives no explicit when-to-use guidance, no prerequisites, and no mention of alternatives among the siblings.

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 real behavior beyond the annotations: it explains the legal basis, enumerates exemptions, recommends a cheapest REP27 combination, and — importantly — discloses that it persists a 'personal page' returned as result_url, which is consistent with readOnlyHint=false. It does not cover cost, rate limits, or that results are advisory rather than legal advice, but the key side effect is disclosed.

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 tightly packed sentences: what is checked, what is produced alongside the verdict (legal basis, exemptions, cheapest combination), and what is returned. Front-loaded with the core action and no wasted words.

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 14 parameters and an output schema in place, the description correctly focuses on outcome shape (verdict + reasoning + exemptions + recommendation + result_url) rather than enumerating inputs. Minor gaps: no note that the result page is persisted/shareable or that output is advisory, which would matter for a legal-adjacent 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 description coverage is 100% across all 14 parameters, so the schema already carries the semantics; the description only gestures at 'a few facts about the company' without adding meaning to individual inputs. Baseline 3 applies when the schema does the heavy lifting.

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?

Specific verb plus resource: it determines whether an EU GDPR Art. 27 representative, UK GDPR Art. 27 representative and/or GPSR Art. 16 responsible person is legally required. The three named obligations and the 'explains why / lists exemptions / recommends cheapest REP27 combination' framing make it clearly distinct from the informational sibling about_rep27.

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 'from a few facts about the company' implies this is the assessment step a user runs when they have company details, but it never states when to use this versus about_rep27 or get_plans, and gives no prerequisites or exclusions. Usage is inferable rather than stated.

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 only declare the generic mutation profile (readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false). The description adds genuinely useful behavior beyond that: payment is completed on an external Mollie page and never in the chat, and the tool returns a payment link rather than a completed transaction. It does not address what happens on repeat calls (duplicate checkouts) or any auth/rate constraints, so it stops short of full coverage.

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 tight sentences with zero filler, front-loading the action and resource before the buyer fields and the external-payment caveat. Every clause carries information an agent can act on.

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 13-parameter (6 required) open-world mutation tool with a full output schema, the description covers the essentials: what is created, with whose data, and where payment occurs. The return-value explanation is redundant given the output schema, and the lack of prerequisite ordering (plan from get_plans) is a minor remaining 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?

Schema description coverage is 100%, so every parameter including enums (service, billing, territory) and defaults is already documented in the schema. The description only echoes the four buyer fields it names and adds no format, constraint, or dependency information beyond the schema, making this the baseline 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 (creates) and resource (the secure Mollie checkout), plus the key inputs and the returned artifact (payment link). It is distinguishable from read-only siblings like get_plans and check_obligations, but it never explicitly contrasts with the nearby sibling start_free_trial, which is the obvious free alternative.

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 'the chosen REP27 plan' implies a plan must first come from get_plans, and 'payment happens on Mollie's page' tells the agent this is the paid path, but there is no explicit when-to-use/when-not guidance or named alternative. Usage is implied rather than stated.

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 covered. The description adds genuine value beyond that by disclosing the shape of the result (the four legal-figure types plus the REP27 service), which the agent would otherwise have to guess. It stops short of noting match confidence or what happens on no-match.

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 sentence that front-loads the verb and resource, then carries the payload in one clause. The parenthetical category list is compact and earns its place by helping an agent judge category coverage.

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 lookup with full schema coverage, complete annotations and an output schema, nothing an agent needs to invoke it correctly is missing. The description supplies the domain framing (REP27, EU legal figures) that the structured fields cannot.

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%, including format hints for 'lang' (ISO 639-1 examples) and free-text multilingual input for 'product'. The description adds nothing beyond the schema on either parameter, 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 ('Searches') and a precisely scoped resource (the REP27 product-category catalogue), enumerated with example categories. It also declares the return payload (EU legal figure + REP27 service), which cleanly separates it from siblings like check_obligations or get_plans.

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 title question and the 'product' parameter, so an agent can infer this is the entry point for 'which legal figure do I need?'. However, no explicit alternatives are named — nothing says when to prefer check_obligations or about_rep27 instead, and no preconditions are stated.

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, closed-world behavior, so the safety bar is met. The description adds real value beyond that: it discloses the currency conversion source (ECB rate) and the constraint that GPSR is always eu territory, which an agent would otherwise not know.

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 sentence that front-loads the returned content and packs scope constraints into the tail with no filler. Every clause 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?

Output schema exists so return shape needn't be explained, annotations cover the safety profile, and all four parameters are fully documented in the schema. The description supplies the remaining business context (what the prices mean, trial/cancellation terms) without gaps.

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 baseline is 3. The description restates territory options and the GDPR/GPSR distinction already documented in the enum descriptions, adding little new parameter meaning beyond 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 a specific verb and resource ('Returns REP27 plans') plus exactly what data comes back: first-year price, renewal price, inclusions, trial and cancellation terms. It is clearly distinguishable from siblings like check_obligations or start_free_trial, though it never names an alternative 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 only implied — an agent can infer this is the pricing lookup tool versus about_rep27 (general info) or create_checkout (purchase). There is no explicit when-to-use, when-not-to-use, or named alternative, which matters given five adjacent tools in a purchase 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 the safety profile (non-readOnly, non-idempotent, open-world), so the bar is lower. The description adds genuinely useful context beyond them: no credit card is needed and the user will receive a follow-up email, which tells the agent the operation is asynchronous and not self-completing.

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 tight sentences with the outcome front-loaded and the downstream email effect last. Nothing is redundant or padding.

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?

An output schema exists, so return values need not be explained, and annotations carry the safety profile. The description is nearly sufficient, though it never mentions the service parameter (which selects the trial product line), leaving an agent to discover that from the 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 100%, so the schema already documents all seven parameters; baseline is 3. The description names only the four required fields (name, email, company, country) and adds a 'work email' nuance, but omits the service, lang, and notes options.

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+resource — 'Starts the REP27 7-day free trial' — with a qualifier ('no card') that hints at how it differs from create_checkout. It does not explicitly name any sibling, but the free-trial framing is unambiguous.

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 name and the '(no card)' qualifier, which suggests this is the frictionless path relative to a paid checkout, but no explicit when-to-use/when-not condition or alternative is given. An agent can infer context but must guess at prerequisites.

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