hr-agent-mcp

analytics_rh

Execute analytical SELECT queries on HR BigQuery data to answer questions about team overtime, delays, and incomplete punches.

Instructions

Executa uma consulta analítica SELECT no data warehouse (BigQuery).

Use para perguntas agregadas sobre a equipe: total de horas extras por mês, evolução de atrasos, batidas incompletas por equipe. Escreva SQL padrão BigQuery usando exclusivamente esta tabela: rh_analytics.agregados_mensais — colunas: equipe STRING (Produto, Engenharia, Comercial), mes STRING 'YYYY-MM', total_atraso_minutos INT64, total_hora_extra_minutos INT64, batidas_incompletas INT64, colaboradores INT64. Somente SELECT é aceito; a consulta passa por validação de governança. Não use WITH/CTE, ponto e vírgula nem comentários — apenas um SELECT direto.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "consulta_sql": "<consulta_sql>"
    },
    "name": "analytics_rh"
  }
}

Input Schema

Name	Required	Description	Default
`consulta_sql`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 17, 2026

TDQS

A4.5/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. Discloses constraints: SELECT only, no CTEs/semicolons/comments, and governance validation. Does not mention rate limits or authentication, but adds valuable 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.

Conciseness5/5

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

Every sentence serves a purpose: action, usage, schema, constraints. No wasted words; front-loaded with the core function.

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?

Covers purpose, usage, SQL details, and constraints. Output schema exists, so return values need not be described. Could explicitly state no side effects, but overall complete for a SQL query tool.

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

Parameters5/5

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

Schema coverage is 0%, but description fully compensates by detailing the required SQL content, exact table and columns, and formatting constraints. Adds critical meaning beyond the generic schema title.

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?

Specific verb 'Executa' and resource 'consulta analítica SELECT no data warehouse (BigQuery)'. Clearly distinguishes from sibling tools like 'aprovar_ajuste' (approvals) and 'consultar_batidas' (time tracking).

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?

Explicit use cases for aggregate queries (horas extras, atrasos, batidas incompletas) and specifies the table and columns. Lacks explicit when-not-to-use or alternative pointers, but sibling context helps.

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

Deploy Server