Retrieve one Lever contact. Default output omits name, headline, emails, and phones; full contact details require detail_profile=full with a reason.
| Name | Required | Description | Default |
|---|---|---|---|
| reason | No | Required when detail_profile requests contact, content, values, or full details. | |
| contact_id | Yes | Lever contact ID. | |
| detail_profile | No | operational returns an operations view; full returns the raw endpoint payload. | operational |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals a non-obvious default behavior (omitting name, headline, emails, phones) and the requirement for detail_profile=full with a reason. This is valuable context, though it does not cover permissions, errors, or rate limits.
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?
The description is two sentences, front-loading the core purpose and then adding one crucial behavioral detail. Every word earns its place with no redundancy or filler.
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?
For a simple get tool with three parameters and no output schema, the description covers the key non-obvious behavior (default field omission and full detail requirement). It does not explain why a reason is needed or what happens when a contact is not found, but these are not critical for basic usage. The description is sufficiently complete for an agent to use the tool effectively.
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 description coverage is 100%, but the description adds meaning by linking detail_profile=full to the need for a reason and explaining the consequence of the default operational profile. This goes beyond simply restating parameter names and provides behavioral context for the parameters.
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 clearly states 'Retrieve one Lever contact' with a specific verb and resource. It distinguishes itself from sibling tools like update_contact and list_* by focusing on a single contact retrieval. The added detail about default output omissions further clarifies the tool's specific behavior.
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?
The description implies when to use the tool (to retrieve a contact) but does not explicitly mention alternatives or exclusions, such as using list_* for multiple contacts or update_contact for modifications. The note about needing detail_profile=full for full details provides some usage guidance, but it is not framed as an alternative comparison.
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.