Skip to main content
Glama

create_requisition

Use Lever POST /requisitions 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.

Schema Changelog

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

  1. First observed

TDQS

C2.5/5.0
Behavior2/5

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

With no annotations, the description carries full transparency burden. It mentions POST, indicating a write operation, but does not disclose side effects, required permissions, error behavior, or that it creates a new requisition. This is a significant gap for a mutation tool.

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 sentence with no wasted words, but it is under-specified rather than effectively concise. It lacks essential information that would make the brevity valuable.

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 tool with 6 parameters, no annotations, and no output schema, the description is critically incomplete. It does not explain what a requisition is, what the body should contain, the outcome of the operation, or when to use dry_run/confirm. The agent would be left guessing on how 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?

Schema coverage is 100%, so the baseline is 3. However, the description adds no meaning beyond the schema. The 'body' parameter is generically described as 'JSON body to send to the documented Lever endpoint' without explaining requisition-specific fields, and the description does not compensate for this vagueness.

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 references the Lever POST /requisitions endpoint, implying the tool creates a requisition, but the phrase 'for recruiting operations' is broad and doesn't explicitly state the action. It also fails to distinguish this tool from siblings like create_posting or create_opportunity.

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 for when to use this tool versus other create tools. The description offers no context about prerequisites, conditions, or alternatives, leaving the agent without direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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