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kula-ai

@kula-ai/mcp-server

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by kula-ai

list_custom_fields

Retrieve custom fields for jobs, candidates, requisitions, or offers with optional filtering by date, department, and office.

Instructions

List custom fields configured in the organization. The type parameter is required — specify job, candidate, requisition, or offer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesSubject type to filter by: job, candidate, requisition, or offer
pageNoPage number
limitNoItems per page
sort_byNoField to sort by (default: created_at)
sort_orderNoSort direction (default: desc)
created_afterNoFilter by created date (ISO 8601, inclusive lower bound)
created_beforeNoFilter by created date (ISO 8601, inclusive upper bound)
updated_afterNoFilter by updated date (ISO 8601, inclusive lower bound)
updated_beforeNoFilter by updated date (ISO 8601, inclusive upper bound)
department_idsNoComma-separated department IDs — returns fields that apply to any of these departments and fields with no department restriction
office_idsNoComma-separated office IDs — returns fields that apply to any of these offices and fields with no office restriction
Behavior2/5

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

No annotations are provided, so the description carries full burden. It only says 'List' which implies read-only, but lacks details on pagination behavior, filtering defaults, or any side effects. More context is needed for an agent to understand the tool's behavior.

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 sentences with no wasted words. The purpose and key constraint are front-loaded. Highly concise and well-structured.

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

Completeness2/5

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

With 11 parameters and no output schema, the description is too minimal. It does not explain output format, pagination behavior, or how filters interact. An agent lacks sufficient context to use the tool effectively.

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 100%, so baseline is 3. The description adds only that 'type' is required, which is already in the schema. No additional parameter meaning beyond the schema.

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 clearly states the tool lists custom fields and specifies the required 'type' parameter with four distinct values, making the purpose unambiguous and distinct from sibling list tools like list_requisition_fields.

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 explicitly states that the 'type' parameter is required and provides the acceptable values, giving clear usage guidance. However, it does not mention when not to use this tool or suggest alternatives.

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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