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huebnermarketing

keka-mcp-server

List Keka Pay Bands

keka_list_pay_bands
Read-onlyIdempotent

Retrieve salary pay bands from Keka to view compensation ranges by job level or grade, including ID, name, minimum and maximum salary, and currency.

Instructions

Retrieve all salary pay bands configured in Keka.

Pay bands define compensation ranges for job levels or grades.

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: Pay bands with ID, name, minimum and maximum salary amounts, and currency.

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

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the description need not repeat safety traits. It adds value by describing the return payload (ID, name, min/max salary, currency) and explaining the domain concept of pay bands, which goes beyond the structured metadata.

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 purpose is front-loaded, the domain explanation is a single useful sentence, and the Args/Returns sections are clearly organized. The parameter list duplicates schema content, but this is acceptable as a quick-reference and does not bloat the description.

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?

For a read-only list tool with three optional parameters and no output schema, the description adequately covers inputs, defaults, and the shape of returned data. It could be more complete by mentioning pagination semantics or contrasting with related salary tools, but nothing essential for invocation is missing.

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?

Input schema coverage is 100%, so the schema already documents all three parameters with types, defaults, and ranges. The description repeats the parameter list but adds no new semantic insight beyond what the schema provides, such as format intent or pagination behavior.

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 opens with a specific verb and resource: 'Retrieve all salary pay bands configured in Keka.' It further clarifies that pay bands are 'compensation ranges for job levels or grades,' which distinguishes this from sibling tools like keka_list_salaries or keka_list_pay_groups.

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

Usage Guidelines2/5

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

No guidance is given about when to prefer this tool over siblings such as keka_list_salaries or keka_list_pay_groups. The description implies a general listing use case but provides no exclusions or alternative routing, leaving the agent to infer appropriateness.

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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