Reajuste de Aluguel

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

Beyond annotations (idempotent, not read-only, not destructive), the description explains the auth flow, distinguishes permanent config-based auth from session-only token pasting, and discloses what happens with no args (returns a link). It doesn't describe all failure/return behaviors, but the essential behavioral traits are visible.

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 information-dense, covering purpose, auth modes, and invocation in a few sentences. The phrasing is slightly run-on, but every major piece of information earns its place and the key guidance 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?

Given the simple one-parameter schema and no output schema, the description covers the two realistic call patterns, the reason for each, and what the no-args call provides. It could be more explicit about success/error responses, but this is a minor gap for a low-complexity auth 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 schema merely defines token as an optional string; the description adds that it is a JWT, that it should be passed after the user pastes it, and that omitting it returns a login link. This gives meaningful semantic context beyond 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?

The description clearly identifies authenticate/login as the tool's purpose, describing browser login and access-token handling. It is specific about the resource (this server's auth/connection) and the action (log in, copy/paste token). It doesn't explicitly differentiate from the sibling 'connect' tool, but the flow is clear enough for an agent.

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 concrete usage guidance: prefer adding the token to the server config for a permanent connection, or paste it for a session-only login. It also tells the agent when to call with { token } versus with no args. It lacks an explicit comparison against sibling tools like connect, but the conditions for invocation are well 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?

Beyond the readOnly/idempotent annotations, the description discloses concrete calculation behavior: it readjusts rent by an index, corrects each overdue month, applies 1% a.m. interest plus a moratory fine, and returns a per-month breakdown and total. No contradiction with the annotations was found.

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 with the domain and legal basis front-loaded, followed by the calculation steps and output shape. Every sentence contributes value and there is no repetition of annotation or schema data.

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?

It states the output shape and the core legal rules, but with nine parameters, no output schema, and zero schema-description coverage, key invocation details such as periodicidade_meses and input date formats remain unexplained. The phrase 'até hoje' also sits awkwardly with the required fim_atraso parameter, creating minor ambiguity about how the end date should be provided.

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%, and the description only partially compensates by explaining the legal formula and mentioning index, interest, fine, and contract/arrears concepts. It does not map parameters like periodicidade_meses, clarify date formats, or state whether juros and multa are optional overrides, so the agent must infer a significant amount from parameter names alone.

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 the tool computes overdue rent under Lei 8.245/91, specifying the steps: index readjustment, monthly correction, 1% monthly interest, and moratory fine. It also names the return format (monthly breakdown plus total), making it immediately distinguishable from the many calculo_* siblings by subject matter.

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 intended scenario—calculating overdue rent—is unmistakable, so an agent can easily choose this over FGTS, pensão, trabalhista, or other sibling calculators. However, it does not explicitly say when not to use it or point to an overlapping sibling such as calculo_indice or calculo_atualizar for pure index/update calculations.

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 readOnly and idempotent annotations, the description discloses critical calculation behavior: it returns per-installment details and totals, and warns that SELIC already includes both correction and interest and that post-09/2024 civil cases use IPCA plus the legal rate. These are non-obvious behavioral rules an agent must know, and nothing contradicts the annotations.

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, front-loaded, and information-dense: purpose first, return behavior second, and critical legal warnings last. Every sentence earns its place, with no filler or repetition of schema details.

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 return-behavior note ('Retorna detalhamento por parcela e totais') is useful, and the legal warnings add essential domain context. However, for a complex 11-parameter calculation tool with zero schema descriptions, the description still leaves too many parameter behaviors implicit to be fully complete on its own.

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%, and with 11 parameters the description needed to compensate. It broadly mentions indices, interest, penalties, and fees, but does not explain the required 'parcelas' structure or key optional parameters like pro_rata, honorarios_tipo, multa_incide_sobre_juros, periodicidade_juros, and data_calculo. This leaves significant semantic gaps for correct invocation.

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 operation: monetary correction of installment payments using an official index and application of interest, penalties, and fees for judicial debt settlement. This clearly distinguishes it from sibling tools like calculo_indice or other calculation tools, even without naming 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?

It clearly states the context: 'Atualização monetária / liquidação de débito judicial' and includes legal-regime cautions that affect calculation. It does not explicitly say when not to use it or name a specific alternative tool, but the intended scenario is well defined.

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 establish read-only, idempotent, non-destructive behavior, lowering the bar. The description adds meaningful process detail: the three-phase sentencing algorithm, the role of attenuating/aggravating circumstances, and configurable fractions, which clarifies what the tool does internally.

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, dense sentence that front-loads the core purpose and then expands on the three-phase method. No wasted words; every clause adds domain-specific 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?

Despite the specific legal framework, the description is incomplete for a 9-parameter tool with no output schema. It does not explain what the tool returns, how to structure multiple causas_aumento/causas_diminuicao items, or the exact meaning of each fraction, leaving an agent to guess at invocation details.

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?

With 0% schema description coverage, the description must compensate, and it partially does by referencing the legal concepts that map to parameters (agravantes, atenuantes, causas de aumento/diminuição). However, it does not explicitly define each parameter—particularly fracao_fase1 and fracao_fase2—leaving ambiguity about which fraction applies to which phase.

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 a criminal sentencing dosimetry calculator based on the three-phase system of art. 68 CP, specifying each phase with legal references. This distinguishes it from sibling calculation tools like calculo_aluguel or calculo_fgts by its unique legal domain.

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 provides clear context: it is for calculating criminal penalties under Brazilian law, which strongly implies when it should be used. However, it does not explicitly state when not to use it or name alternative tools, leaving exclusion to inference.

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 non-destructive behavior. The description adds useful behavioral context: the calculation is per deposit, applies 3% a.a. interest, and currently accepts manual deposits only, with OCR mentioned as future capability. It does not detail return shape or edge cases, but it adds value beyond the annotations without contradicting them.

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: two sentences with the essential purpose front-loaded and the OCR limitation in a parenthetical. Every phrase contributes information, and there is no repetition of schema or annotation content.

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 4-parameter calculation tool with no output schema, the description covers the core computation and main inputs but omits the output shape and the role of `data_calculo`; default index behavior is also implied rather than stated. The annotations reduce the need for side-effect disclosure, but an agent would still have to infer some invocation details.

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 compensate. It alludes to the `indice` choice ('índice de inflação vs TR'), the `depositos` list ('por depósito', 'depósitos manuais'), and interest behavior ('juros de 3% a.a.'). However, it does not clarify `data_calculo`, the structure of `competencia`/`valor`, or default behavior when optional parameters are omitted.

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 'Correção do FGTS (tese TR → INPC/IPCA-E, STF)', which immediately identifies the specific subject and legal context. 'Por depósito calcula a diferença' states the precise computation, making it clearly distinguishable from sibling calculation tools like calculo_revisional or calculo_indice.

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 establishes a clear use context: FGTS correction comparing inflation-index correction against TR, with per-deposit granularity. It does not explicitly name alternatives or exclusions, so it stops short of a 5, but the intended invocation scenario 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?

Beyond the read-only/idempotent/destructive annotations, the description discloses the non-obvious BACEN/IBGE date convention (start month excluded, end month included) and the conditional output behavior with 'valor' and 'incluir_valores'. No contradiction with annotations — 'consulta' matches readOnlyHint=true. It does not address defaults or error behavior, but the core behavioral traits are well covered.

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 dense sentences with no filler. The first sentence front-loads the purpose and the critical date convention; the second adds the conditional invocation modes. 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?

The tool has 6 parameters, no output schema, and moderate complexity. The description covers purpose, date convention, and output modes, and hints at return values. But it leaves pro_rata unexplained, the default behavior when data_final is omitted (it is optional) unstated, and the unusual 'NENHUM' enum value unaddressed — notably, 'retorna o valor corrigido' only describes the value path, not what result shape the base query 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?

With 0% schema description coverage, the description carries the full burden. It enriches most parameters: the date convention defines data_inicial/data_final semantics, 'valor' maps to corrected-value output, and 'incluir_valores' maps to monthly variations. However, pro_rata — a parameter that materially alters how the correction is computed — is left completely undocumented, a meaningful gap at this coverage level.

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 ('Consulta de índice oficial') and resource (accumulated correction factor between two dates), with an exact date convention (initial month excluded, final month included). This clearly differentiates it from the many calculo_* siblings covering aluguel, FGTS, pensão, trabalhista, etc., none of which deal with official price indices.

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 conditional mode guidance — 'Se valor informado, retorna o valor corrigido; se incluir_valores true, retorna as variações mensais' — which tells an agent how to elicit different behaviors. However, it provides no explicit when-to-use vs alternatives or exclusions; the domain is implied by 'índice oficial' rather than contrasted with 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 indicate readOnlyHint, idempotentHint, and non-destructive behavior, so the description does not need to restate safety. It adds useful behavioral context: default treatment varies by regime and can be overridden per asset with 'entra_partilha'. No contradiction with annotations.

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 compact sentences: the first front-loads the purpose and the computation, the second gives a specific override instruction. No filler or repetition.

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?

The description conveys the core legal calculation and the override mechanism, but the tool has nested objects, no output schema, and zero schema descriptions. It lacks detail on return structure, the meaning of regime variants, and how optional fields affect the calculation, so the agent may need external legal knowledge to invoke it 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?

With 0% schema description coverage, the burden is on the description. It gives meaning to 'bens', 'dívidas', 'regime', and the 'entra_partilha' overrides, but does not explain 'nomes' or the many legal attribute flags inside asset/debt items, such as 'oneroso', 'incomunicavel', or 'heranca_doacao'. Partial compensation only.

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 specifies a unique domain (divorce property division under the Civil Code) and a concrete computation: determining the shared estate and each spouse's share, with an adjustment for imbalance. This distinguishes it from sibling calculation tools without requiring 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?

It states the context ('no divórcio') and the legal basis ('por regime (Código Civil)'), and gives an operational instruction for overriding defaults via 'entra_partilha'. It does not explicitly name alternatives or exclusion cases, but the domain context is clear enough among 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 establish read-only, idempotent, non-destructive behavior. The description adds valuable algorithmic detail: per-month calculation based on % of minimum wage/fixed value/% of remuneration, subtraction of payments, INPC correction, and 1% monthly interest from each due date. Minor transparency gap: it presents INPC and 1% as fixed while the schema exposes juros and indice parameters, which could mislead an agent into thinking they are not configurable.

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 dense sentence with no filler. It front-loads the purpose and each clause adds necessary algorithmic information: what is calculated, what is subtracted, and what correction/interest is applied. Every part 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?

Given the tool's complexity (9 parameters, arrays, enums, no output schema), the description provides the core algorithm but is incomplete. It does not describe the return value structure, the meaning of data_calculo/referencia, or how competencia strings in the arrays are formatted. These gaps could leave an agent unsure how to correctly populate all inputs.

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 compensate, and it does substantially. It explains the meaning of forma (the three modes), pagamentos (subtracted), indice/juros (correction + interest), and the period (inicio/fim). However, it does not explain referencia, data_calculo, or how remuneracoes array is used in detail, leaving some parameters under-specified.

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 calculates overdue alimony (pensão alimentícia em atraso) per month, subtracts payments, and applies correction and interest. It clearly distinguishes itself from sibling tools by subject matter and legal basis (art. 528 CPC), leaving no ambiguity about what it does.

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 provides clear context: this tool is for alimony arrears calculation. It does not explicitly name alternatives or when-not-to-use, but the specific legal article and subject make the appropriate use case evident. This meets the 'clear context, no exclusions' threshold.

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 destrutiveHint, so the safety profile is covered. The description adds operational detail by listing the calculation dimensions and the discounts for remição and detração, but it does not disclose output format or edge-case assumptions.

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 dense sentence that front-loads the purpose and packs in the legal factors without unnecessary filler. It could be better structured for readability, but every part contributes meaning.

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 9-parameter legal calculator with no output schema, this description is incomplete. It provides the formula outline and the date output, but omits required input semantics such as the unit of pena_anos and the expected format of inicio_cumprimento, and it never describes the return structure.

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 compensate by explaining the parameters. It maps many of them (reincidente, hediondo, violência, resultado_morte, dias_trabalhados, hooras_estudo, dias_detração), but it does not explicitly cover the required pena_anos and inicio_cumprimento, nor their units/date format.

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 a specific legal resource ('Progressão de regime'), cites the governing statute, and states that the tool returns the progression date. It is clearly distinguishable from the sibling calculo_* tools, though it does not explicitly contrast itself with an 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 intended use is inferred from the legal formula and the output: use this when a prison-regime progression date must be calculated. There is no explicit when-to-use/when-not-to-use guidance and no comparison with the adjacent calculo_dosimetria 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?

Annotations already establish readOnly=true and idempotent=true, so the safety profile is known. The description adds useful behavioral context beyond annotations by defining the calculation as summing corrected discounted installments and by specifying the administrative legal basis, which is non-obvious. It does not contradict the annotations.

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 only two sentences, front-loads the core purpose, then adds the legal context. Every sentence contributes information and there is 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?

Although the annotations cover side effects, there is no output schema, and the description leaves important operational details unexplained: how indices affect the correction, how data_calculo is used, and what the result looks like. For a tool with an array input and an enum index parameter, the description is informative but not complete enough for an agent to invoke it confidently.

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 compensate, but it only hints at the semantics of the main 'descontos' array through 'soma as parcels descontadas'. It never explains the 'ind' meaning beyond the vague word 'corrigidas', and 'data_calculo' is completely unaddessed. This is insufficient for the three parameters.

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 explains the tool's purpose: calculating restitution of undue INSS discounts by summing corrected installments, and it anchors the tool in a specific legal context (STF agreement ADPF 1.236) with specific INSS codes, which distinguishes it from the many sibling calculation tools. This is a specific resource plus detailed scope, not a placeholder.

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 provides clear context for when to use the tool: for administrative INSS refund scenarios involving 'fraude associativa' and the listed codes 280/304/310/378. However, it does not explicitly name sibling tools or state when NOT to use it, so it falls short of full alternative-based 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?

The annotations already cover read-only, idempotent, and non-destructive behavior. The description adds meaningful context beyond those annotations by disclosing a live BACEN rate lookup depencency and the legal nuance that the average rate is a reference, not a ceiling. This is useful behavioral information for an agent.

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 concise and front-loaded: it states the purpose in the first few words, then adds live-lookup and legal context in two tight sentences. Every sentence contributes useful information without repetition or 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 and 8 parameters, the description covers the core purpose and an important legal caveat, but it does not describe the return structure or clarify optional inputs like taxa_bacen_am as an override versus the live lookup. It is adequate for basic invocation but leaves gaps for a fully autonomous agent.

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 compensate. It clarifies the roles of sistema (Price or SAC), modalidade+mês for the live lookup, and the concept of excedente por parcela. However, it does not explain key parameters like taxa_bacen_am, data_contrato, valor_financiado, or parcela_paga, relying mostly on their self-evident names.

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 specific verbs and resources: 'recalcula o financiamento' and 'apura o excedente por parcela', and identifies the distinctive BACEN market-rate calculation. It clearly distinguishes this tool from the many other calculo_ siblings by focusing on bank-contract revisional calculations.

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 frames the exact use case: revisional de contrato bancário with BACEN average rates, which gives a routing agent a clear context for selecting it. However, it does not explicitly name alternatives or state when not to use this tool, so it falls short of the highest bar.

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?

The readOnlyHint, idempotentHint, and destructiveHint annotations already cover the safety profile, and the description does not contradict them. It adds behavioral context by stating the 5% limit and 'restituição corrigida dos descontos', which helps an agent understand what kind of output to expect. It still leaves the exact calculation behavior and return format unspecified, but for a read-only calc tool this is acceptable.

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, front-loaded with the central acronym, and every fragment adds domain context; there is no obvious filler. The 'Tese' sentence is a little elliptical, but it earns its place by explaining the legal rationale behind the calculation.

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 should explain what result an agent will receive, but it only vaguely mentions limits and corrected restitution. It also omits how required inputs relate to the calculation, how the descontos array should be structured, and how the optional enums affect results, leaving the tool incomplete for confident 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?

With schema description coverage at 0%, the description carries the full burden of explaining inputs, but it only alludes to 'descontos' and INSS benefit without defining the required beneficio_mensal, descontos structure, competencia, indice, data_calculo, or the role of tipo. The '5%' hint and 'RMC/RCC' enum relation add some domain meaning, but largely not enough for an agent to populate the parameters correctly from prose alone.

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 domain (RMC/RCC for INSS, codes 217/268), the calculation (5% margin limit) and the output concept (corrected restitution of discounts), so an agent can tell this is a specific legal-calculation tool. It lacks an explicit action verb, but the tool name and the description together make the purpose reasonably unambiguous. It doesn't explicitly contrast itself with siblings, though RMC/RCC is a distinct subject among the calculo_* tools.

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 contextual grounding – INSS benefit, codes 217/268, and a specific legal thesis about revolving credit sold as consigned – which implies when the tool is relevant. However, it never states an explicit selection rule or names alternative tools that should be used instead, so an agent has to infer applicability rather than be told 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?

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds behavioral context beyond annotations by disclosing the coefficient formula, the 20H/15M threshold, and the floor/ceiling clamping behavior, which helps the agent understand how inputs affect the result.

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 dense sentence with no filler. It front-loads the tool's purpose and packs the legal basis, formula, coefficient rule, and floor/teto behavior into minimal words while remaining readable for a domain-aware agent.

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 calculation tool, the formula, thresholds, and floor/ceiling behavior are well specified. However, the schema shows teto_inss and salario_minimo as optional, and the description does not clarify whether defaults are applied when omitted. There is no output schema, so an explicit statement of the returned value would have improved completeness.

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 carries the full burden of explaining parameters. It successfully maps all five: media_salarios is the average salary, tempo_contribuicao_anos plus sexo drive the coefficient via the 20H/15M threshold, salario_minimo is the floor, and teto_inss is the ceiling. This fully compensates for the absent schema 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?

The description clearly defines the resource (RMI — Renda Mensal Inicial) and specifies the exact calculation methodology: average salary × coefficient with floor and ceiling. It distinguishes itself from sibling tools by naming a specific INSS benefit and the post-reform EC 103/2019 rule, which is not a tautology.

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 through the formula and RMI specificity, but the description never explicitly states when to use this tool instead of related siblings like calculo_pensao or calculo_rmc_rcc. There are no exclusions or alternative tool references, so the agent must infer the appropriate context.

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, and non-destrutivo, so safety is covered. The description adds context that the value is dynamic and comes from IPEADATA, which is useful, but it does not clarify return format, behavior for invalid or unspecified years, or external-data dependencies beyond the source name.

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, focused sentence with no wasted words. It communicates the resource, the dynamic data source, and the main legal applications efficiently.

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 lookup, the description names the source and the fields/results relevance, but it does not state the output shape or the behavior when no year is provided. With no output schema present, those gaps leave some ambiguity for an agent invoking the tool autonomously.

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 one parameter, ano, with zero description coverage. The description's phrase 'de um ano' does map to the intended parameter, but it does not explain whether the parameter is optional, what happens when omitted, or what year range is accepted. Since the parameter name is self-explanatory, this is adequate but not thorough.

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 resource and outcome: the current national minimum wage for a year, sourced dynamically from IPEADATA. It is specific enough to distinguish this tool from the sibling calculo_* tools, although it uses a noun phrase rather than an explicit verb such as 'returns' or 'calculates'.

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 explicitly gives use cases: it is the basis for dosimetria (dia-multa), penso por % do salário mínimo, and mnimo existencial. This tells an agent when the tool is relevant, though it does not contrast it with alternatives like calculo_pensao or calculo_dosimetria.

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 establish that this is read-only, idempotent, and non-destructive. The description adds useful behavioral context beyond the annotations: the R$600 existential minimum is parameterizable and the payment-capacity analysis considers plans of up to 5 years. It does not describe the exact return format, but the safety profile is already covered by annotations.

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, front-loaded sentence: it opens with the domain and legal basis, then lists the output concepts after a colon. Every word carries meaning, and there is no repetition of the tool name or 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?

The description is adequate for a read-only calculation tool: it names the legal rule, the default minimum existential value, and the expected output concepts. It lacks an explicit mapping to required and optional parameters and does not describe the response structure, which matters because 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?

With 0% schema description coverage, the description must compensate for missing parameter documentation. It explicitly explains `minimo_existencial` via 'R$600, parametrizável' and hints at `prazo_meses` via 'plano de até 5 anos', while `renda_liquida` and `dividas` are only indirectly inferable from 'renda comprometida/disponível'. It does not state required inputs or describe the fields of the `dividas` array.

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 domain (superendividamento), cites the applicable law, and lists the computed outputs: percentage of committed income, existential minimum, available income, and payment capacity. It clearly differentiates this tool from sibling calculation tools by topic, though it stops short of a 5 because it lacks an explicit verb such as 'calcula'.

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 'Superendividamento (Lei 14.181/2021)' provides clear legal context for when this tool should be used, and no sibling tool appears to cover the same subject. It does not explicitly mention alternatives or state when not to use it, so it falls just short of full 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?

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds important non-obvious behavior: EC 103/2019 conversion factors, the 13/11/2019 cutoff, concurrency counting, and the return of total time plus old-rule threshold. This goes well beyond what annotations and schema alone provide.

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 dense, front-loaded sentence: purpose first, then algorithm, then return values. Every clause adds useful information and there is no filler or repetition of schema metadata.

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 tool with no output schema, the description covers the core algorithm, the special-activity date cutoff, and the return intent (total time and whether old threshold is reached). It does not specify exact output field names, units, or edge-case behavior, but it is substantially complete for 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 description coverage is 0%, so the description must compensate. It does link sexo to the 35H/30M threshold and interprets vínculos as contribution links with special-activity conversion. However, it does not explain the vinculos item fields (inicio, fim, tipo) or expected date format beyond what the raw schema structure provides.

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 a specific resource (tempo de contribuição/CNIS) and states exact operations: summing bonds, counting simultaneity once, and converting special activity to common time. This clearly distinguishes it from sibling calculation tools like calculo_pensao or calculo_rmi.

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 clear context: this tool is for CNIS contribution time and returns whether the old 35H/30M threshold is reached. It does not explicitly name alternatives or state when not to use it, but the scope is sufficiently clear to guide selection.

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 destructiveHint, so the safety profile is covered. The description adds meaningful behavioral context by specifying that INSS and IRRF calculations use 2026 tables, that FGTS penalties can be 40%/20%, and that indemnity payments are exempt, which goes beyond the annotations.

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 dense, well-organized sentence that front-loads the tool's purpose and then lists the covered items in a compact semicolon-separated sequence. Every phrase earns its place, with no filler or redundant elaboration.

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 complex labor-law calculation with nine parameters, no output schema, and no parameter descriptions, the high-level scope is insufficient for reliable invocation. Required inputs like salario, admissao, and demissao have no format guidance, and optional flags such as projetar_aviso are unexplained. The annotation safety profile is covered, but invocation semantics remain incomplete.

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%, and the description does not explain the nine parameters. It mentions components like aviso prévio and férias vencidas, which hint at aviso and ferias_vencidas, but it does not clarify the format or meaning of salario, admissao, demissao, motivo, projetar_aviso, dependentes, or saldo_fgts. This leaves agents to guess parameter 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?

The description clearly identifies a specific calculation domain: Brazilian CLT termination severance (verbas rescisórias), and enumerates the exact components it covers, such as salary balance, indemnified prior notice, proportional 13th, vacations, FGTS penalty, and INSS/IRRF deductions. This level of specificity distinguishes it from sibling tools like calculo_fgts, calculo_aluguel, and calculo_pensao.

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 usage for labor termination settlements but does not explicitly state when to use this tool versus alternatives such as calculo_fgts or calculo_revisional. There is no when-to-use or when-not-to-use guidance, so an agent must infer selection from the domain label rather than receiving explicit routing instructions.

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 readiness and idempotency, so the description does not need to repeat those. It adds useful conditional behavior: the response shape differs depending on whether all providers are connected or credentials are missing, including the presence of connect_url and per-install URLs. This goes beyond the structured fields without contradicting them.

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, front-loaded with the core purpose, and wastes no words. Each clause adds meaningful information about the response under different conditions, making it concise and well-structured.

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 simple, zero-parameter, read-only status tool, the description covers the two main response scenarios. It leaves minor ambiguity around partial connectivity states and the exact structure of 'per-install URLs', but this is not critical given the tool's simplicity and the annotations covering safety and idempotency.

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 description coverage is trivially 100%, so there is nothing the description must clarify about parameters. Per the rubric, a no-parameter tool earns a baseline of 4, and the description's focus on return behavior is appropriate here.

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 the tool returns connection status and URLs, which is a specific verb and resource. It implicitly differentiates itself from sibling tools like authenticate by describing status checking rather than performing authentication, but it does not explicitly name or contrast any sibling, so it stops short of 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?

The description provides no direct guidance on when to use this tool versus alternatives such as authenticate or show_version. It explains conditional outputs, which gives context about behavior, but an agent must infer that this is for checking connection status rather than acting on it. No exclusions or alternative routing are provided.

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, the description discloses several non-obvious behaviors: invoke runs a tool one-off even when the MCP is not installed, returns a connect link when credentials are needed and a checkout/top-up link when the wallet is empty, requires retry after the user opens the link, carries permission restrictions on writes, and yields shareable prompt-library links that open without login. This is rich behavioral context that annotations alone do not provide.

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 the core purpose and every clause carries a distinct fact, but it is written as a single run-on paragraph with some mixed-language and garbled artifacts like 'pontualmente' and 'the one-off install behind invoke'. Given the tool's complexity, a more structured or bulleted layout would significantly improve scannability.

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?

The sweep of the description covers the core flow, the invoke-vs-install distinction, permission requirements, auth/payment edge cases, and the prompt library. However, with 14 actions and 23 parameters and no output schema, several actions like resume, report_bug, list_tools, and request_mcp are not named or explained, and no return-value details are given. It is enough for the main happy path but not for full tool coverage.

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 across 23 parameters, and the description compensates only for the central ones: action values (search, describe, invoke, install, prompt actions), query as intent, mcp_id/tool_id in the flow, and prompt_slug/body for publishing. Most parameters such as arguments, prompt_vars, conversation, immediate, tier_slug, cancel_reason, request_details, and cancel_comment remain unexplained, so the compensation is partial at best.

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 'official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them' and then details the search→describe→invoke core flow plus the prompt library. It names specific verbs and resources for each mode (search, describe, invoke, install, publish_prompt) and is clearly distinguished from the sibling calculator tools.

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 explicitly routes between actions: 'search discovers MCPs by intent → describe returns one MCP's full profile... → invoke RUNS that tool', and it states 'Use install only to make an MCP PERMANENT... prefer invoke for a single/occasional use'. It also notes that writes require workspace owner/admin. However, it does not explicitly say when to bypass marketplace for already-installed sibling tools, so the alternatives are not fully enumerated.

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 tool as non-read-only, idempotent, and non-destructive. The description adds the useful context that the conversation array is included 'for reproduction,' but does not disclose other side effects, such as where the report is sent or whether users receive confirmation. This is adequate for a simple reporting tool but not rich.

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 short sentences with no filler. The purpose is front-loaded, and the reproduction instruction is placed directly after it. Every sentence contributes.

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?

Given a simple three-parameter tool with no output schema, the description covers the core purpose and the most important optional input. However, the 'context' field is undocumented, and a required 'message' field relies on inference rather than explicit definition. This leaves clear but minor 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 0%, so the description must carry parameter meaning. It explains the 'conversation' parameter as recent messages for reproduction, but leaves 'context' completely unexplained and only implies that 'message' is the report text. This is partial compensation for one of three parameters.

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 action and resource: 'Report a bug, missing feature, or send feedback.' It is immediately distinct from the sibling calculo_* tools, which are all calculation-related. The purpose 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?

The description explicitly enumerates the situations where the tool applies: bug reports, missing features, and feedback. It includes a concrete reproduction instruction, telling the agent to attach the conversation array. It does not name alternatives or exclusions, but none are needed given the unrelated sibling set.

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 destructiveHint, covering the safety profile. The description adds the 'current' qualifier, implying a live query, but it does not describe output format or potential failure modes. Given the annotations, this is adequate but not rich.

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, concise sentence with no filler or redundant information. The key resource is placed front and center.

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, read-only version tool, this description is sufficient for an agent to invoke it correctly. However, the return value shape is not described and there is no output schema to fill that 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?

The tool has 0 parameters and the schema is fully covered, so the description does not need to explain parameter meaning. The baseline for a zero-parameter tool 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 uses a specific verb ('Show') and names the exact resource ('current MCP platform and adapter versions'). This clearly distinguishes it from the calculation, authentication, and reporting siblings.

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 use when version information is needed, but it does not explicitly say when to use this tool versus alternatives like toolkit_info. No exclusions or conditional guidance are provided.

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, and destructiveHint=false, so the safety profile is established. The description adds useful context about what information is returned, but does not go deeper into behaviors such as whether authentication is required or how current the state is at call time.

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, well-structured sentence that front-loads the main action and resource, then enumerates the specific pieces of state returned. Every word contributes meaning; there is no redundancy or 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, read-only info tool, the description fully explains what the agent can expect to learn by calling it: installed MCPs, connection status, connected accounts, and catalog tool counts. No output schema exists, but the listed return categories provide sufficient contextual completeness.

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?

This tool has zero parameters, so the baseline of 4 applies. The description correctly focuses on the output rather than parameters, and there is no parameter ambiguity to resolve.

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 ('Returns') and identifies a clear resource ('current toolkit state') with explicit listed contents: installed MCPs, connection status, accounts, and catalog tool counts. This clearly distinguishes toolkit_info from siblings like show_version or connect, which serve different purposes.

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 this tool is for inspecting the overall toolkit state, which is useful context for when an agent needs to check connections or available MCPs. However, it does not explicitly state when to use this tool instead of siblings like connect, authenticate, or marketplace, nor does it mention any exclusions.

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