Calculadora Jurídica

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

The description adds useful behavior beyond the annotations: config header yields a permanent connection, while passing a token yields a session-only login, and no args returns a link. It does not fully spell out side effects or success/failure return values, but annotations already cover idempotency and non-destructiveness.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense and front-loaded, with no fluff, but the long single sentence with parenthetical clauses and multiple alternatives could be structured into clearer separate instructions. Still, every part adds necessary 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 one-parameter auth tool with no output schema, it covers the no-arg return (the link), the token-paste path, and the persistent-config alternative. It doesn't state the response on a token success/failure, but the invocation guidance is sufficient for an agent to call 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 coverage, the description carries the full burden for the optional `token` parameter. It explains that token is a JWT/access token pasted by the user and how to pass it, compensating well for the bare 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 the tool as MCP.AI authentication for IDE agents, with a concrete browser-login + access-token flow and two invocation paths (no args for a link, token for login). This specific verb+resource is unambiguous and easily distinguished from the unrelated calculo_* sibling 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?

It explicitly differentiates the persistent config-header approach ('best... permanent, non-expiring') from the session-only paste/login path, and states exactly when to call with no args versus with { token }. This gives the agent clear selection criteria for both setup and invocation.

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 description discloses the full calculation behavior: index-based adjustment over the contract, correction of each overdue month through today, 1% monthly mora interest, late-payment penalty, and monthly-plus-total output. This goes well beyond the readOnlyHint/idempotentHint annotations and is consistent with them, with no contradiction.

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 front-loads the use case and legal basis, then describes the calculation steps in order, and ends with the return shape. No filler or repetition; every clause 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?

The description is adequate to identify the tool and understand its main behavior, but for a complex legal-financial calculation with 9 parameters, no output schema, and no per-property descriptions, it leaves notable gaps: no default assumptions when optional parameters are omitted, no date format guidance, and only a vague 'detalhamento por mês + total' for 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. It adds meaning to core parameters: aluguel_inicial, inicio_contrato, inicio_atraso/fim_atraso, juros (1% a.m.), multa, and indice are all referenced directly. However, it does not explain optional parameter semantics such as periodicidade_meses, whether juros/multa are overrides or defaults, or expected date formats, leaving gaps for a 9-parameter tool.

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 resource ('Aluguéis em atraso (Lei 8.245/91)') and a clear set of actions: it adjusts the rent by the index, corrects each overdue month until today, applies 1% monthly mora interest and a late-payment penalty, and returns a monthly breakdown plus total. This clearly distinguishes it from sibling tools like calculo_indice or calculo_atualizar.

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 opening phrase 'Aluguéis em atraso' clearly signals the intended scenario: overdue rent calculations under Brazil's Lease Law. It does not explicitly name alternatives or state when not to use it, so it falls short of full routing guidance, but the context is unambiguous enough for an agent to select it over related calculo_* 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?

Beyond the read-only/idempotent/destructive annotations, the description discloses return behavior ('detalhamento por parcela e totais') and the crucial SELIC double-counting trap plus the Lei 14.905/2024 regime. This materially shapes how an agent should configure and interpret the calculation.

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 short, information-dense segments: scope/action, return shape, and critical legal warning. No filler; every sentence carries load.

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 11-parameter calculation with no output schema, the description gives purpose, output shape, and the legal caveats that prevent miscomputation. It stops short of documenting defaults (e.g. data_calculo/pro-rata) and edge-case routing, leaving some burden on the 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?

With 0% schema description coverage, the description compensates by explaining the role of index, interest, penalty, fees, and parcelas, and by clarifying the SELIC/interest relationship and post-2024 index choice. It does not spell out every parameter such as pro_rata or multa_incide_sobre_juros, but the core legal semantics are conveyed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific scope: 'Atualização monetária / liquidação de débito judicial', names the resource ('parcelas'), and states the operations: correction by official index plus interest, fine, and fees. This clearly differentiates it from siblings like calculo_indice or calculo_trabalhista without requiring schema inspection.

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 provides a clear use context (judicial debt settlement/monetary update) and critical legal handling rules: SELIC already bundles correction+interest, and post-09/2024 civil cases should use IPCA + legal rate. It does not explicitly name sibling alternatives or exclusions, so it stops short of full routing 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 the tool read-only, idempotent, and non-destructive. The description adds meaningful behavioral context by describing the three calculation phases and noting that fractions are configurable. There is no contradiction with 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?

One compact, information-dense sentence with no filler. The legal basis is front-loaded, the phases are presented in order, and the mention of configurable fractions is essential rather than redundant.

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 provides strong domain context for a complex legal calculator, but it is operationally incomplete: there is no output schema, no return format, no default fraction values, and no guidance on how to fill the array fields such as causas_aumento and causas_diminuicao.

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 most of the semantic weight. It maps phases to likely parameter groups (judicial circumstances, aggravating/mitigating factors, increase/decrease causes) and mentions configurable fractions, but it does not explain fraction format/units, defaults, or how pena_min_anos and pena_max_anos are used.

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's purpose: calculating criminal sentence dosimetry under the Brazilian three-phase system (art. 68 CP). It is far more specific than a tautology and provides concrete legal context, but it does not explicitly distinguish itself from criminal-law siblings such as calculo_progressao.

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 legal context implies when the tool should be used, but the description does not state explicit conditions, exclusions, or alternatives. An agent must infer that this is for sentencing calculations rather than other criminal-law 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?

As anotações já cobrem readOnlyHint, idempotentHint e destructiveHint. A descrição adiciona contexto comportamental valioso: cálculo por depósito, juros de 3% a.a., depósitos manuais e OCR apenas futuramente. Não contradiz as anotações.

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?

Duas frases curtas, sem repetição do nome da ferramenta e com a informação central apresentada logo no início. A menção ao OCR futuro é contextual e não polui a definição.

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?

Não há output schema nem descrição explícita do retorno, e os detalhes de parâmetros são insuficientes. Para uma ferramenta com 4 parâmetros e um array obrigatório de depósitos, faltam informações sobre formato dos itens, saída e possíveis padrões ou defaults.

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?

A cobertura de descrição no schema é 0%, então a descrição deveria compensar. Ela menciona conceitos como depósitos, índice de inflação e juros de 3%, mas não enumera os quatro parâmetros nem explica a estrutura esperada de cada depósito. Fica uma lacuna relevante para invocação correta.

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?

A descrição tem verbo específico ('calcula a diferença'), recurso específico (FGTS) e contexto jurídico delimitado (tese TR → INPC/IPCA-E, STF). Isso a distingue claramente de irmãos genéricos como calculo_indice, calculo_revisional e calculo_trabalhista.

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?

O contexto de uso fica claro: correção de FGTS segundo a tese do STF, por depósito. Porém, não há exclusões explícitas nem menção a alternativas ou situações em que outro cálculo deveria ser usado.

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 mark the operation read-only and idempotent, and the description adds genuinely non-obvious behavior: the asymmetric month convention (initial month excluded, final month included) and the conditional outputs when valor or incluir_valores are set. These are exactly the behavioral quirks that would cause wrong results if left undisclosed.

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 short sentences, front-loaded with the core purpose, then branching into the two conditional behaviors. Every sentence earns its place; the date convention and the operational hint 'puxar os índices crus' are high-value density.

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 solid on core semantics but leaves real gaps: pro_rata is unexplained, the meaning of the NENHUM enum option is unclear, and with data_final optional an agent is left guessing the default period. With no output schema, the return shape is also undisclosed.

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 and largely pays it: it explains that valor triggers a corrected-value result, incluir_valores triggers monthly variations, and it pins the meaning of the date pair via the month convention. However, pro_rata (a boolean that plausibly controls pro-rata correction) is never mentioned.

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?

'Consulta de índice oficial' names a specific verb and resource, and the description defines the exact computation: accumulated correction factor between two dates under the BACEN/IBGE convention (initial month excluded, final month included). This scope cleanly separates it from sibling calculo_* tools, which address other domains like rent or FGTS.

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 makes the tool's domain (official index correction) clear enough that an agent can infer when to pick it over calculo_aluguel or calculo_fgts, but it never states an explicit when-to-use or when-not-to-use rule and names no alternative tool. Usage context 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 establish read-only, idempotent, non-destructive behavior. The description adds beyond that: it reveals the core calculation (assets minus debts according to regime), the concept of compensation for imbalance, and the override behavior for 'entra_partilha'. There is 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 tightly written sentences front-load the core purpose and legal basis, then give the most important user-actionable instruction. No filler or repetition; every clause 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?

The description gives the algorithm and the override mechanism, which is a solid baseline. However, with no output schema and no parameter descriptions, an agent is left without the return shape, field-level input semantics, or examples of how regime defaults differ. For a legal calculation involving nested assets and debts, this is helpful but not 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?

Schema description coverage is 0%, so the description carries the burden of explaining parameters. It mentions 'bens' and 'dívidas' conceptually and explains 'entra_partilha', but it leaves 'regime' somewhat implicit, does not explain 'nomes', and does not clarify nested fields like 'titular', 'oneroso', 'heranca_doacao', or 'adquirido_na_constancia'. The guidance is too thin for a schema with four top-level parameters and nested objects.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific legal operation: 'Partilha de bens no divórcio por regime (Código Civil)', then explains it computes the shareable estate, each spouse's share, and equalization payment. This clearly distinguishes it from sibling calculation tools (e.g., pensão, FGTS, trabalhista) by naming both the resource and the specific legal calculation.

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: use for divorce asset division under a marriage regime, and it instructs the user to mark 'entra_partilha' per asset to override the regime default. It does not explicitly name sibling tools to avoid or give when-not-to-use conditions, so it stops one step short of full explicit routing.

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 annotations, the description discloses the calculation pipeline: monthly due amount, subtraction of payments, INPC correction, and 1% monthly interest from each maturity. It does not detail every edge case such as payment ordering or rounding, but it gives substantial behavioral context.

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 packs the subject, legal basis, timeframe, calculation modes, payment deduction, correction index, and interest rule. There is no filler and the core information 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?

For a complex nine-parameter calculator with no output schema, the description provides most of the needed context: what is calculated, how, over what period, and with what corrections. It stops short of describing the return shape and the exact meaning of 'referencia' and 'data_calculo', which prevents a perfect score.

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 text compensates by explaining the forma modes ('% do salário mínimo', 'valor fixo', '% da remuneração'), the role of payments, the correction index, and the interest rate. The required 'referencia' parameter is not named explicitly and 'data_calculo' is absent, but most inputs are inferable from the described formula.

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 domain ('Pensão alimentícia em atraso'), a specific legal basis (art. 528 CPC), and a concrete action ('calcula o devido'), with an explicit formula. This distinguishes it from sibling calculo_* tools such as calculo_aluguel or calculo_fgts without ambiguity.

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 intended scenario: overdue alimony payments, calculated month by month. It does not explicitly name alternative tools or say when not to use it, but the legal and factual context is specific enough to route an agent correctly.

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 description discloses the core behavior beyond the annotations: it computes a completion percentage using stated factors, discounts remição and detração, and returns a date. Annotations already mark it read-only and idempotent, and nothing in the description contradicts 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?

One dense, front-loaded sentence that packs legal basis, formula variables, discounting behavior, and return value with no filler. Every clause 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?

For a 9-parameter calculator with no output schema, the description covers the legal context, the main input factors, the discounting logic, and the output. It leaves minor gaps such as exact required-parameter mapping and return structure, but annotations cover the safety profile, making the overall guidance sufficient.

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 semantic burden. It compensates by mapping domain concepts to the parameters (primário/reincidente maps to reincidente, comum/hediondo to hediondo, violência/resultado morte to violencia/resultado_morte, trabalho/estudo to hours/days worked, detração to dias_detracao). It does not explicitly state units or date formats, but the conceptual mapping is strong.

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 computation ('Progressão de regime'), cites the controlling legal provisions (LEP art. 112, Lei 13.964/2019), and enumerates the factors that distinguish it from sibling calculators (recidivism, heinous crime, violence/death). It also states the output (progressão date), so an agent can tell exactly what the tool 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 legal-citation-heavy description makes the intended use clear: calculating Brazilian prison regime progression. It does not explicitly say when not to use it or name an alternative (e.g., calculo_dosimetria), so it lacks the explicit routing of the strongest definitions.

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, non-destructive and idempotent. The description adds meaningful behavior beyond that: it sums and applies monetary correction to the discounted installments, and ties the calculation to the STF agreement framework. It doesn't cover edge cases or output details, but the safety profile is already carried 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?

Two dense sentences, no filler. The scoped purpose and legal context are front-loaded, and both sentences add selection-relevant 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 strong annotations, the tool has no output schema and near-zero parameter descriptions, so the description must cover invocation details. It provides the domain context but not the parameter semantics, correction-index behavior, or return value, making it incomplete for reliable agent 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 0% schema-description coverage, the description must define the parameters, but it only indirectly refers to the discount list ('parcelas descontadas') and the idea of correction ('corrigidas'). It never explains the 'indice' choices, 'data_calculo', or how 'codigo/competencia/valor' are used, leaving an agent under-informed for 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 states a specific action ('Restituição de descontos indevidos no INSS') with a concrete computation ('soma as parcelas descontadas corrigidas') and scopes it to exact discount codes (280/304/310/378). This clearly differentiates it from sibling calculo_* tools, e.g. calculo_indice or calculo_fgts, even without inspecting schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear when-to-use context: INSS undue discounts from associative fraud codes and the ADPF 1.236/STF administrative reimbursement pathway. It does not explicitly name alternative tools or state when not to use it, so it misses the top level of guidance, but the use case is unambiguous.

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

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint values. The description adds valuable behavioral context: it performs a live BACEN lookup by modality+month, supports Price or SAC, and clarifies that the average rate is a reference, not a ceiling, per STJ. There is 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?

The description is two sentences with no filler. It front-loads the core purpose, then adds the key calculation method and an important legal caveat. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The annotations cover the side-effect profile, and the description explains the core algorithm well. Still, with no output schema and 8 parameters whose schema descriptions are absent, the description omits the output format and the behavior/meaning of several optional parameters. It is usable for invocation but not 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?

Schema description coverage is 0%, so the description must compensate. It adds meaning by tying modalidade+mês to the live BACEN lookup and Price/SAC to the sistema parameter, and 'excedente por parcela' clarifies parcela_paga. However, it does not map all 8 parameters or explain optional fields like taxa_bacen_am and data_contrato in detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Revisional de contrato bancário' and states a specific verb: 'recalcula o financiamento' and 'apura o excedente por parcela'. The resource and scope are clear, and the bank-contract-revision domain distinguishes it from the sibling calculo_* tools without ambiguity.

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 'Revisional de contrato bancário' clearly establishes when this tool applies: bank loan contract revision using BACEN average rates. It does not explicitly name alternatives or exclusions, so it stops short of a 5, but the use case is clearly framed.

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 mark it read-only, idempotent, and non-destructive, so no safety disclosure is missing. The description adds useful behavioral context: the 5% limit, corrected restitution, and the thesis that the revolving card sold as consigned never amortizes, which clarifies the calculation's 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?

Two compact sentences front-load the domain and key calculation factors. The legal thesis sentence adds context without bloat, so the description is efficient.

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 with five parameters, no output schema, and zero parameter descriptions, the description is too sparse to fully guide invocation. It omits what the tool returns, how the descontos array is interpreted, and the meaning of the indice/tipo enums; annotations compensate only for safety, not for these 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 carries the burden, and it only loosely maps to parameters: '5%' relates to beneficio_mensal, 'descontos' appears in the schema, and 'corrigida' hints at indice. It does not explain tipo, indice choices, data_calculo, or the descontos item structure, leaving several 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 names RMC/RCC and the INSS consignment margin domain with specific codes (217/268), making the calculation's subject unmistakable. It doesn't use an explicit verb like 'calculates', but 'limites de 5% e restituição corrigida' conveys the computation. It is clearly distinct from the sibling 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 implies the tool is for RMC/RCC margin/restitution calculations and mentions the legal thesis, but it never states when to prefer this tool over a sibling or when not to use it. No alternatives or exclusions are given, so routing must be inferred from the domain 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?

Beyond the readOnly/idempotent/non-destructive annotations, the description fully discloses the calculation behavior: base average, gender-specific coefficient thresholds, and floor/cap clamping. An agent can predict exactly what computation will happen, and the annotations already rule out side effects.

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 entire behavior fits in one dense, front-loaded sentence with no filler. Every clause adds a structural part of the calculation, from inputs to coefficient to floor/cap constraints.

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 5-parameter calculator with no parameter descriptions and no output schema, the description covers the core calculation, input roles, and floor/cap behavior. It could add explicit parameter units or edge-case notes, but it is largely sufficient for an agent to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema description coverage is 0%, the description compensates by mapping all five parameters: media_salarios, tempo_contribuicao_anos and sexo via the coefficient rule, and salario_minimo/teto_inss as piso/teto. It does not explicitly state units or formats, but the formula and schema types make these mostly inferable.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the exact output — Renda Mensal Inicial under EC 103/2019 — and gives the precise formula: average salary × coefficient, with minimum wage floor and INSS cap. The '20H/15M' gender-specific threshold and piso/teto terms make it clearly distinct from sibling 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 provides clear context: it is for post-reform RMI calculation using contribution average, coefficient, minimum wage floor, and INSS cap. It does not explicitly name sibling alternatives or say when not to use it, but the formula leaves little ambiguity about the intended use.

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 and destructiveHint=false, covering the safety profile. The description adds that the data is dynamic and comes from IPEADATA, which is useful context beyond the annotations, but it does not disclose behavior such as return format, error handling, or data availability windows.

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 short sentences, front-loaded with the core definition and followed by application context. Every sentence earns its place; no redundant wording.

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 single-parameter read-only tool, the description covers what it returns and why it matters, but it omits the return format and what happens when 'ano' is omitted (schema marks it optional). No output schema exists, so these details would need to be in the description to be 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?

With schema description coverage at 0%, the description must compensate. 'um ano' implies the 'ano' parameter refers to the target year, but it does not specify valid year ranges, whether the parameter is required (schema says optional), or formatting expectations. The meaning is inferable but not fully 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 identifies the resource as the national minimum wage in effect for a given year, sourced dynamically from IPEADATA. This is more specific than the tool name alone and clarifies the domain (legal sentencing, alimony, and existential minimum calculations), though it does not explicitly contrast with sibling 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 concrete use contexts: 'Base para dosimetria (dia-multa), pensao por % do SM e minimo existencial', indicating when to invoke it. It does not name alternatives or exclusions, so it stops short of a 5.

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

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

Annotations already establish read-only/idempotent behavior, and the description adds useful context about the computed metrics: committed income percentage, R$600 parameterizable minimum existential, available income, and payment capacity. It does not disclose formulas, default values beyond R$600, or exactly how the 5-year plan is capped, but the read-only annotation lowers the burden.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the legal subject; each clause adds a distinct metric or parameter context. It is a fragment rather than a complete sentence, but it contains 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?

There is no output schema, and the description only summarizes outputs at a high level. Given a required debt array and no parameter documentation, the definition is not detailed enough for a reliable first call, even though tool selection is straightforward.

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 needed to explain inputs like renda_liquida, dividas, and prazo_meses, but it only hints at income and a 5-year plan. It does not clarify the debt structure fields (saldo, parcela, credor) or required conditions, leaving an agent to infer most parameter semantics from variable 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 names the specific legal subject ('Superendividamento (Lei 14.181/2021)') and enumerates the computed outputs: committed income percentage, minimum existential, available income, and payment capacity. This clearly distinguishes it from sibling calculo_* tools such as calculo_aluguel or calculo_trabalhista, although it lacks an explicit verb like '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 legal citation and the concepts of mínimo existencial and payment capacity imply use for Brazilian over-indebtedness cases under Lei 14.181/2021. However, it does not explicitly state when not to use it or name alternative tools, so the guidance remains implied rather than directive.

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 (readOnlyHint, idempotentHint, non-destructive), the description discloses important behavioral details: concurrent vínculos are counted once, special activity conversion is limited to 13/11/2019, and the output indicates both total time and whether the old-time threshold is reached. 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?

The description is two tight sentences, front-loading the domain and then providing the algorithm and outputs. There is no filler, and every clause adds operational 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 tool with no output schema, the description explains the return values (total time and whether the old threshold is reached) and covers the key algorithm cases: concurrency, special activity conversion, date cutoff, and sex-specific thresholds. It is slightly incomplete on parameter formatting and output units, but the core behavior is well covered.

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 explains vínculos conceptually and hints at sex via 35H/30M, but it does not explain the `inicio`, `fim`, or `tipo` fields of the vínculos items, the enum meanings, or the expected date format. This is insufficient parameter guidance.

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 resource ('Tempo de contribuição (CNIS)') and states concrete operations: summing vínculos, counting concurrent periods once, and converting special activity to common using EC 103/2019 factors. It also states what the tool returns, making its purpose clear and distinguishable from sibling calculators.

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 the use case through CNIS, vínculos, and the 35H/30M old-time threshold, but it never explicitly states when to choose this tool over a sibling calculator or gives exclusions/prerequisites. Usage context is present but only implied.

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. The description adds useful behavioral detail beyond that: it discloses INSS/IRRF deductions using 2026 tables, the FGTS fine rates, and that indemnity items are exempt. 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?

The description is compact: two sentences, front-loaded with the core domain, followed by an organized list of items. Every phrase adds information, including the law reference and the 2026 table detail. No filler or redundancy.

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 9 parameters, 0% schema coverage, and no output schema, the description is adequate as a high-level overview but not fully self-sufficient for nuanced calls. It does not explain required input formats, the meaning of 'projetar_aviso', or how 'motivo' influences the FGTS fine and tax treatment.

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 for the 9 parameters, but it only loosely maps to a few of them (aviso, férias, FGTS). It leaves 'motivo', 'saldo_fgts', 'projetar_aviso', 'dependentes', and date formats unexplained, and it only names the 'indenizado' aviso option, potentially obscuring the other enum values.

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 scope: 'Verbas rescisórias / liquidação trabalhista (CLT)' and enumerates the main calculation components (saldo de salário, aviso prévio, 13º, férias, FGTS). It is unambiguous about the tool's purpose, though it lacks an explicit verb and does not explicitly contrast it with sibling calculators.

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 domain statement implies when to use the tool — for CLT termination/labor settlement calculations — but it does not state explicit conditions, prerequisites, or alternatives such as calculo_fgts or calculo_salario_minimo. There is clear contextual scope but no exclusions or sibling routing.

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 this is read-only, idempotent, and non-destructive. The description adds useful behavioral detail beyond that by specifying the two main response states: authenticated:true with empty pending[] when all providers are connected, and connect_url plus per-install URLs when credentials are missing. This helps an agent predict what to expect.

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-loads the core purpose, and then adds only the essential conditional details. Every sentence contributes meaningful information, and there is no waste.

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 status tool with no output schema, the description is complete enough. It tells the agent what information will be returned, what the success condition looks like, and what happens when credentials are missing. The low complexity means no additional guidance is required.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the description does not need to explain any input semantics. The baseline of 4 applies because there is no parameter burden at all.

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's purpose: returning connection status and URLs. It distinguishes connect from its sibling authenticate by framing it as a status/read operation rather than an action, and the conditional output descriptions reinforce this.

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 makes clear that this is the tool to call when checking connection state or getting URLs. It does not explicitly mention alternatives like authenticate, but the context strongly implies connect is for status checking rather than initiating authentication, so usage is clear without being fully explicit.

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 important behaviors: invoke runs an MCP even when it is not installed, does a one-off run without adding the MCP to the toolkit, returns a connect link when credentials are needed, returns a checkout/top-up link when payment is needed, and requires workspace owner/admin for write operations. The description enriches the annotations and does not contradict 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 front-loaded with identity and the core flow, and nearly every sentence carries useful guidance. However, it is one dense, wall-of-text paragraph with mixed language ("pontualmente") and heavy inline emphasis, which makes the many action alternatives hard to scan and parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex 23-parameter, 14-action facade with no output schema, the description is remarkably complete: it covers the core flow, one-off invoke semantics, auth/credential/payment behavior, permission requirements, installed flags, the prompt library, and most action outcomes. The main gaps are the resume action and return-shape details for a few actions, but the overall guidance is sufficient for correct invocation in most cases.

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 does a lot of compensating work: it maps action values such as search, describe, invoke, install, list_tools, publish_prompt, and explains tool_id, arguments, and prompt-related intent. However, several parameters and enum actions remain unexplained, including resume, limit, immediate, tier_slug, cancel_reason, report_context, conversation, request_name, and request_details, leaving agents under-specified for those paths.

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 the official mcp.ai marketplace: the in-platform catalog of MCPs/tools and the way to run them. It states the core discovery→describe→invoke flow, distinguishes the prompt-library subdomain from the MCP flow, and makes it clear this is a marketplace orchestrator rather than one of the sibling calculator/authenticate 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 explicit when-to-use guidance: use install only to make an MCP permanent, prefer invoke for one-off use, use list_tools to see what is callable now, use subscribe/cancel for billing, and use request_mcp when nothing fits. It also explains what to do when invoke returns a connect link or checkout link, including retry behavior.

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 carry the safety profile with idempotentHint=true and destructiveHint=false. The description adds that conversation data is needed for reproduction, which is useful context. However, it does not disclose what happens after submission, such as whether a ticket is created or whether the report is asynchronous, though the annotations lower the burden.

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 consists of two tight sentences: the first states the purpose, the second gives the key usage instruction. There is no filler, repetition, or irrelevant detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple three-parameter reporting tool with annotations already covering idempotency and destructiveness, the description is mostly sufficient. The main gaps are the unexplained `context` parameter and the absence of any indication of what the response or outcome will be, though no output schema is 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 compensate for undocumented parameters. It only clarifies the `conversation` parameter via 'conversation array with recent messages,' leaving the required `message` and optional `context` undefined. The agent must guess at their intended content.

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 the verb 'Report' and explicitly enumerates three targets: 'bug, missing feature, or send feedback'. This makes the tool's purpose unmistakable and easily distinguishable from the sibling calculo_* and authentication tools, which serve entirely different functions.

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 context: use when a user reports a problem or wants to provide feedback. It also adds practical guidance to 'Include the conversation array with recent messages for reproduction.' It does not name alternatives, but none of the sibling tools overlap with bug reporting, so exclusions are unnecessary.

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 declare readOnlyHint=true and idempotentHint=true, so the agent knows this is a safe, non-mutating call. The description adds little beyond that—it names the output as versions but doesn't specify the format (e.g., semver strings, JSON object) or whether the output is human-readable. Since the annotations carry the safety profile, a 3 is appropriate.

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 sentence of 9 words, front-loading the action ('Show') and the object ('version'). There is zero waste, and it fully conveys the tool's purpose within its scope. This is a model of conciseness.

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, idempotent tool with no output schema, the description is nearly complete. An agent can confidently invoke it without additional context. The only minor gap is that the return format is unspecified, but since there is no output schema, a brief note on the output structure (e.g., 'returns a plain-text summary') would elevate completeness. Still, the description is sufficient for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, and the schema coverage is 100% (no properties). The description doesn't need to explain parameters. The baseline for zero-parameter tools is 4, and the description is consistent with that—it correctly implies that no input is required.

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's purpose: 'Show the current MCP platform and adapter versions.' This is a specific verb-resource pair that distinguishes it from sibling tools, which are all calculation or authentication tools. It could be slightly more explicit about what 'show' returns (e.g., a text summary vs. structured data), but the resource 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 implies that this tool is for checking version information, which makes sense in contexts where an agent needs to confirm platform/adapter versions before proceeding. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention whether version information is needed for authentication or compatibility checks. Given the sibling tools are all calculations, the usage context is reasonably clear, but not explicitly delineated.

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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description does not need to restate safety. It adds value by detailing what kind of state is returned, including connection status and account bindings, which helps the agent understand the tool's informational scope.

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 tightly packed sentence with the main action front-loaded, followed by a colon-delimited list of return contents. Every phrase earns its place with no 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?

For a zero-parameter, read-only introspection tool, the description fully covers what the agent needs to know before calling: what information it will receive. No output schema exists, but the description essentially provides a light output contract by enumerating the returned components.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema is empty with zero parameters, and schema description coverage is 100%, so the description has no parameter burden. Per calibration, zero-parameter tools receive a baseline of 4; the description's output-focused content is more than sufficient.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Returns') and resource ('current toolkit state'), then enumerates exactly what is included: installed MCPs, connection status, connected accounts, and catalog tool counts. This is specific enough to distinguish it from computational siblings like calculo_* and action tools like authenticate or connect.

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 clearly conveys that this is the tool to call when an agent needs an overview or snapshot of the toolkit's current state. It does not explicitly list exclusion criteria or name alternatives such as show_version, but the context is clear enough for routine selection.

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