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update_opportunity_interview

Use Lever PUT /opportunities/:opportunity/interviews/:record 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_idYesLever record ID for this collection.
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

C2.4/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It only mentions the HTTP method PUT, which implies a write operation, but it doesn't describe side effects, required body structure, permissions, or return values. This is minimal transparency beyond what the tool name already conveys.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, short sentence, which is concise and front-loaded with the endpoint. However, it sacrifices substance for brevity, providing little more than a pointer to the API. Every sentence should earn its place, and this one barely does.

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

Completeness1/5

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

This is a mutating tool with 4 required parameters and a nested body object, yet the description gives no context about how to construct the body, what the endpoint expects, or the safeguards like dry_run and confirm. The absence of an output schema and annotations makes this description severely incomplete for an agent to invoke the tool correctly.

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 has 100% parameter description coverage, so the schema handles parameter semantics. The description adds no parameter-level meaning beyond the endpoint reference, so the baseline score of 3 is appropriate.

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

Purpose3/5

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

The description is a bare reference to the Lever API endpoint ('Use Lever PUT /opportunities/:opportunity/interviews/:record') rather than a clear statement of what the tool does. It doesn't explicitly say 'update an interview' in plain language, though the resource is inferable from the endpoint. It's a step above tautology because it names the HTTP method and resource path.

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 provided on when to use this tool versus alternatives. The phrase 'for recruiting operations' is too generic to help an agent decide between this and other update tools like update_opportunity_feedback or update_opportunity_panel.

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