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huebnermarketing

keka-mcp-server

List Keka Pay Groups

keka_list_pay_groups
Read-onlyIdempotent

Retrieve configured payroll groups to identify pay cycles and filter salary listings by pay group ID.

Instructions

Retrieve all payroll groups configured in Keka.

Pay groups define payroll cycles and are used to filter salary listings.

Args:

  • pageNumber (integer): Page number (default: 1)

  • pageSize (integer): Results per page, max 200 (default: 100)

  • response_format ('markdown' | 'json'): Output format (default: 'markdown')

Returns: List of pay groups with IDs, names, and descriptions. Use IDs with keka_list_salaries to filter by pay group.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageSizeNoResults per page (max 200)
pageNumberNoPage number (starts at 1)
response_formatNoOutput format: 'markdown' for human-readable, 'json' for machine-readablemarkdown

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds return-shape details and the relationship to salary listings, but does not disclose deeper behavioral traits such as pagination limits or data freshness beyond what the schema already provides.

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

Conciseness4/5

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

The description is well-structured and front-loaded, with a clear purpose, Args section, Returns line, and downstream usage note. The Args block repeats schema information, causing minor redundancy, but the overall size is reasonable and no filler is present.

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

Completeness5/5

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

For a simple read-only list tool with no output schema, the description is sufficiently complete: it explains what is returned, lists the relevant parameters, and connects the IDs to keka_list_salaries. Annotations cover the safety profile, and no critical missing context is apparent.

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 coverage is 100%, so the baseline is 3. The description restates the parameter meanings but adds no new semantic details beyond the schema; the 'pay groups define payroll cycles' context helps slightly but does not deepen parameter understanding.

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

Purpose4/5

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

Description clearly states the action ('Retrieve') and resource ('all payroll groups configured in Keka'). It distinguishes the tool from siblings by domain concept ('pay groups define payroll cycles'), though it doesn't explicitly name a sibling alternative.

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?

The description gives clear context: pay groups are used to filter salary listings, and it explicitly points to keka_list_salaries for downstream use. It lacks explicit when-not-to-use guidance against siblings like keka_list_pay_bands, so it stops 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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