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get_opportunity_interview

Retrieve one Lever interview by ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
interview_idYesLever interview ID.
opportunity_idYesLever opportunity ID.

Schema Changelog

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

  1. First observed

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It only says 'Retrieve,' which essentially restates the tool's name ('get'), and adds no disclosure about return format, error behavior, or required permissions. Without an output schema, the agent has no idea what response to expect.

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, front-loaded sentence with no wasted words. Every word earns its place: verb, resource, scope, and lookup method. It is appropriately sized for the tool's simple nature.

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

Completeness3/5

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

For a simple get-by-ID tool with no annotations and no output schema, the description is minimally sufficient but not complete. It does not mention that the interview belongs to an opportunity (though the name implies it), nor does it describe the expected return value. The absence of output schema makes this a notable gap, though the schema covers required parameters.

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 description adds no parameter-level meaning beyond the schema, which already documents both parameters with concise descriptions. Since schema coverage is 100%, the baseline of 3 applies; the description's phrase 'by ID' does not clarify which ID maps to which parameter, but the schema names them clearly.

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 uses the specific verb 'Retrieve' and clearly identifies the resource as 'one Lever interview,' distinguishing it from the sibling list_opportunity_interviews and other get_* tools. It explicitly states the scope ('one') and the lookup mechanism ('by ID'), making the purpose unambiguous.

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?

No explicit guidance is provided about when to use this tool versus alternatives like list_opportunity_interviews. The intended usage is implied from 'one' and 'by ID,' but the description does not mention alternative tools or exclusion criteria, leaving the agent to infer context.

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