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by kula-ai

list_application_interviews

Retrieve all interviews for a specific job application, with options to filter by status, date, interviewer, or location.

Instructions

List the interviews scheduled on a specific application. Same as list_interviews but scoped to one application (the application_id goes in the path, not as a filter). Cancelled interviews are included by default — use meeting_status=cancelled etc. to filter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
application_idYesID of the application (candidate's submission to a job). Use list_applications to discover.
pageNoPage number (default: 1)
limitNoItems per page (default: 20, max: 100)
interviewer_idsNoComma-separated user IDs of participants
organizer_idsNoComma-separated user IDs (the user who scheduled the interview)
meeting_statusNoComma-separated statuses: not_started, in_progress, ended, cancelled, candidate_no_show
kindNoComma-separated kinds: one_on_one, panel, external
locationNoComma-separated locations: onsite, phone, zoom, google_meet, microsoft_teams, hackerrank
ai_note_taker_enabledNoFilter by AI note-taker flag
start_time_afterNoInclusive lower bound on start_time (ISO 8601)
start_time_beforeNoInclusive upper bound on start_time (ISO 8601)
created_afterNoFilter by created date (ISO 8601 inclusive)
created_beforeNoFilter by created date (ISO 8601 inclusive)
updated_afterNoFilter by updated date (ISO 8601 inclusive)
updated_beforeNoFilter by updated date (ISO 8601 inclusive)
sort_byNoSort field (default: created_at)
sort_orderNoSort direction (default: desc)
Behavior4/5

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

With no annotations, the description carries the burden. It discloses that cancelled interviews are included by default, which is a key behavioral trait. Could mention pagination limits but schema covers that.

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, no fluff. First sentence states purpose and differentiation, second adds key default and filtering guidance. Efficient and front-loaded.

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 listing tool with 17 parameters and no output schema, the description covers the most important context: scope differentiation and default filter behavior. Could expand minimally, but sufficient.

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

Parameters4/5

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

Schema description coverage is 100%, baseline 3. The description adds value by noting that application_id goes in the path and suggesting list_applications to discover it, going beyond 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 'List the interviews scheduled on a specific application' and directly distinguishes itself from the sibling tool 'list_interviews' by specifying the scope and parameter placement.

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?

Provides context on default behavior (cancelled interviews included) and how to filter with meeting_status. Implies when to use this tool over list_interviews, though does not explicitly state when not to.

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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