get_falazuki_info
Informações sobre o Fala, Zuki! — assistente financeiro pessoal no WhatsApp. Preço, funcionalidades, como começar.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Informações sobre o Fala, Zuki! — assistente financeiro pessoal no WhatsApp. Preço, funcionalidades, como começar.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It correctly labels the tool as informational by listing content categories, but it does not mention response format, whether the information is static, or explicitly confirm that no side effects occur.
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, efficient sentence immediately states the subject and the three key content areas: price, features, and how to start. There is no filler or redundant 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 no-parameter informational tool, the description provides essential context: it identifies what the tool is about and what information it returns. The only minor gap is that it does not describe the response shape, but the tool's low complexity makes this acceptable.
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 100% schema description coverage, so there are no undocumented inputs. Per the baseline for zero-parameter tools, the description need not add parameter detail, and it does not attempt to.
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 (Fala, Zuki! — a personal finance assistant on WhatsApp) and the exact content categories: price, features, and how to start. This is distinct from the many calculation and advice siblings, so an agent can easily recognize its purpose.
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 when to use the tool: when the user asks about Fala, Zuki! itself, such as pricing, features, or getting started. However, it does not explicitly state when not to use it or name any alternative tools, leaving some inference to the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools are clearly distinct, but the set contains several near-identical clusters: calculate_dividend_income_goal and calculate_dividend_yield both answer 'how much capital is needed to reach a dividend income target', and calculate_real_salary, calculate_raise_vs_inflation, and calculate_salary_time_value overlap heavily on salary/inflation comparisons. Generic tools like compare_investments and compare_with_cdb also blur the boundary with the many specific yield calculators.
Most tools follow a clear verb_noun snake_case pattern with verbs like calculate_, get_, check_, compare_, and advise_. However, the object language is inconsistent (calculate_ganho_capital_imovel alongside calculate_car_affordability), and can_i_quit_job breaks the command-style pattern with a question.
At 114 tools, the server is extremely over-scoped for an MCP surface; an agent cannot reasonably hold all these options in context. The inclusion of a search_calculator tool to route among the others is a strong signal that the tool set itself needs partitioning.
The surface is very comprehensive for Brazilian personal finance: employment, taxes, investments, debt, real estate, vehicles, small business, insurance, and market-data queries are all covered. A few minor gaps exist, such as no dedicated generic boleto-fine calculator or consolidated investment comparison engine, but no core workflow feels badly stranded.