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list_opportunity_interviews

List interviews for a Lever opportunity, including schedule metadata, panel/interviewer references, and feedback template references.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoResults per page. Lever accepts 1-100; default is endpoint-specific.
cursorNoLever pagination offset token from a previous response. Use next_cursor from the prior tool result.
opportunity_idYesLever opportunity ID.

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It usefully discloses that responses include schedule metadata and references (not full details) for panels and feedback templates. However, it does not mention pagination behavior, result limits, or the read-only nature beyond the verb 'list'.

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?

The description is a single sentence of 15 words, front-loaded with the verb and resource, and contains no redundant or filler information. Every phrase adds value.

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 simple list tool with no annotations and no output schema, the description states the purpose and hints at response contents (schedule metadata, references). It does not explicitly outline the return structure or pagination, but the schema covers parameter details. This is adequate for a list endpoint, though slightly more detail on response shape would improve completeness.

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?

The input schema already provides descriptions for all three parameters (limit, cursor, opportunity_id), giving 100% coverage. The description adds no additional parameter semantics beyond identifying the resource as 'a Lever opportunity'.

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 interviews for a Lever opportunity' with a specific verb and resource. It further distinguishes itself from siblings like list_opportunity_notes or list_opportunity_feedback by mentioning the included metadata (schedule, panel/interviewer references, feedback template references).

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

Usage Guidelines3/5

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

The description implies usage for retrieving interview data for an opportunity but does not explicitly state when to use this tool versus alternatives such as get_opportunity_interview (single interview) or other list endpoints. There are no exclusions or alternative guidance.

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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TDQS

B3.1/5.0
Disambiguation4/5

Most tools target distinct resource-action combinations, but the sheer count (108) and the presence of closely related tools like list_opportunity_feedback / get_opportunity_feedback may cause occasional agent confusion.

Naming Consistency5/5

Tool names follow a highly consistent verb_noun pattern (e.g., create_*, get_*, list_*, update_*, delete_*, add_*, remove_*). Minor exceptions like apply_to_posting still fit the overall structure.

Tool Count2/5

With 108 tools, the surface is excessively large for most agent workflows. Many tools could be merged or removed without losing essential functionality, leading to decision overload.

Completeness5/5

The tool set covers the full Lever API surface comprehensively, including opportunities, postings, requisitions, users, webhooks, templates, files, and compliance data, leaving no obvious gaps.

Resources