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create_feedback_template

Use Lever POST /feedback_templates 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.
actor_idYesLever user ID associated with this action when needed.
template_idNoUnused for create operations.

Schema Changelog

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

  1. First observed

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations, the description was expected to disclose behavioral aspects such as side effects, authorization needs, or error conditions. It only mentions the HTTP endpoint, offering no additional transparency about the operation's behavior.

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 very brief and front-loaded with the endpoint, but it is too sparse to be appropriately informative. It wastes no words, yet the single sentence provides minimal value beyond the tool name.

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?

Given the complexity of six parameters, a nested body object, and no output schema, the description fails to explain what a feedback template is, how to structure the body, or any constraints. It is incomplete for a tool with such requirements.

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 baseline is 3. The description adds no parameter-specific meaning beyond the schema, but the schema already documents all six parameters, including template_id being unused for create operations.

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 clearly indicates this creates a feedback template via the Lever POST /feedback_templates endpoint. It identifies the specific verb and resource, distinguishing it from sibling tools like create_form_template or create_opportunity_feedback, though it does not explicitly define what a feedback template is.

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, nor any prerequisites or context. The phrase 'for recruiting operations' is vague and does not clarify scenarios or exclusions.

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