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create_opportunity_interview

Use Lever POST /opportunities/:opportunity/interviews for recruiting operations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYesJSON body to send to the documented Lever endpoint.
reasonNoReason for this Lever write.Requested through Lever Ops Control Plane.
confirmNoSet false only when you explicitly want to block execution.
dry_runNoWhen true, preview the write without sending it to Lever.
record_idNoUnused for create operations.
perform_asYesLever user ID for perform_as when the Lever endpoint needs one.
opportunity_idYesLever opportunity ID.

Schema Changelog

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

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

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

Since no annotations are provided, the description must carry the full behavioral disclosure burden. It only reveals the HTTP method and endpoint, implying a write operation, but does not mention required permissions, side effects (e.g., calendar invitations, notifications), reversibility, or response behavior. This is minimal transparency.

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 description is a single concise sentence with no wasted words and is front-loaded with the operation and endpoint. It is appropriately short, although it sacrifices informative content for brevity.

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?

For a write tool with seven parameters, a nested body object, no annotations, and no output schema, this description is far too sparse. It references the endpoint but fails to explain what an interview creation entails, what the body should contain, or what the agent should do with the response. The description is a stub rather than a complete operational guide.

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 the schema already documents all seven parameters, including opportunity_id, perform_as, and body. The description adds no parameter-level meaning, but the baseline of 3 applies because the schema handles the heavy lifting. The free-form 'body' parameter remains opaque, but that is an API documentation concern rather than a schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the exact Lever endpoint (POST /opportunities/:opportunity/interviews) and the sibling set includes many opportunity sub-resources, so it is clear this creates an interview for an opportunity. It could be more explicit by saying 'creates an interview,' but the HTTP method and path plus the tool name provide sufficient specificity.

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 use this tool versus alternatives like update_opportunity_interview, delete_opportunity_interview, or list_opportunity_interviews. The phrase 'for recruiting operations' is too generic to help an agent decide between creating, fetching, updating, or deleting an interview.

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