List Lever API-created webhooks. Default output shows event and URL host only; full URLs/configuration require detail_profile=full with a reason.
| Name | Required | Description | Default |
|---|---|---|---|
| reason | No | Required when detail_profile requests contact, content, values, or full details. | |
| 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 burden of disclosing behavior. It explicitly states that default output is limited to event and URL host, and that full details require a specific parameter and a reason. This goes beyond the schema by explaining the practical behavior of the default vs. full output. It does not mention pagination or authentication, but the main behavioral trait is covered.
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 long, front-loaded with the core purpose, and then provides the essential nuance about default behavior and full detail requirements. Every sentence earns its place without redundancy or unnecessary detail.
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?
The tool has no output schema, and the description covers the main purpose, output limitations, and parameter behavior. It is sufficiently complete for a simple list tool with 2 parameters. Minor gaps like pagination or error handling are not critical for this context.
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%, so baseline is 3. The description adds value by explaining what the output looks like in default mode versus full mode, and by confirming that a reason is needed for full detail. This complements the schema's parameter descriptions by connecting them to the actual data returned.
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 starts with a specific verb and resource: 'List Lever API-created webhooks.' This clearly distinguishes it from sibling tools like create_webhook, delete_webhook, and update_webhooks. It also adds scope by specifying 'Lever API-created' webhooks, which may be a distinct subset.
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 provides clear context on when to use the tool (listing webhooks) and gives usage guidance on how to get full details ('full URLs/configuration require detail_profile=full with a reason'). However, it does not explicitly mention alternatives or when not to use it, but for a list tool the usage is apparent.
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.