Retrieve the Lever diversity survey configuration for a posting. Requires a compliance reason and acknowledge_sensitive_data=true.
| Name | Required | Description | Default |
|---|---|---|---|
| reason | Yes | Compliance reason for reading diversity survey configuration. | |
| posting_id | Yes | Lever posting ID. | |
| acknowledge_sensitive_data | No | Must be true for high-sensitivity reads such as EEO PII, diversity surveys, or download URLs. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that reading this configuration requires a compliance reason and explicit sensitive-data acknowledgment, indicating a sensitive/audited operation. However, it does not describe return format or error behavior; 'Retrieve' implies read-only.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single, front-loaded sentence that states the action and necessary prerequisites with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose and prerequisites for a simple retrieval operation. With no output schema, the agent may not know the exact response shape, but all parameters are documented and the operation is straightforward enough to be largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and each parameter already has a meaningful description. The description repeats the key requirements without adding new syntactic or semantic detail beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Retrieve' and names the exact resource 'Lever diversity survey configuration for a posting', clearly distinguishing it from sibling get/list tools like get_posting or list_eeo_responses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context for when to use the tool and states required parameters (compliance reason, acknowledge_sensitive_data=true), but does not explicitly mention alternative tools or exclusions. The resource is specific enough that the intended use is evident.
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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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.
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.
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.
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.