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wganalytics

mcp-crewai-giulia-ai

by wganalytics

buscar_dados_sql

Converts natural-language questions into validated read-only SQL and executes them on a specified PostgreSQL database to return query results.

Instructions

Responde uma pergunta em linguagem natural consultando o banco indicado.

O SQL é gerado a partir do schema, validado como somente-leitura e executado numa sessão read-only. Use listar_databases para descobrir os valores de database_name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
database_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does substantial work: it discloses that SQL is generated from the schema, validated as read-only, and executed in a read-only session, giving the agent a clear safety profile for a query tool. It omits auth/permission requirements, rate limits, and error behavior, so it is strong but not exhaustive.

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

Conciseness5/5

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

Two short sentences, front-loaded with the core purpose followed by the read-only guarantee and the sibling pointer. No filler text.

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

Completeness4/5

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

An output schema exists, so return values need not be explained, and the description covers purpose, safety guarantees, and parameter discovery. What remains missing is any note on permissions, limits, or failure modes for a tool that runs generated SQL, keeping it just under fully complete.

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

Parameters3/5

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 that 'query' is a natural-language question rather than raw SQL, which is valuable, and that 'database_name' is a value discoverable via listar_databases. It does not specify the expected format or naming convention for database_name, leaving a real gap.

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

Purpose5/5

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

The description states a specific verb and resource: it answers a natural-language question by querying a given database. It also implicitly separates itself from the sibling by telling the agent to use listar_databases for database_name values, so the boundary between the two tools is clear without opening either schema.

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

Usage Guidelines4/5

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

It gives explicit routing guidance for the database_name parameter ('Use listar_databases para descobrir os valores de database_name'), which is the main selection decision here. It does not, however, state when this tool is inappropriate or describe exclusions, so it falls short of a full 5.

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

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