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get_opportunity_feedback

Retrieve one Lever feedback form. Default output keeps scores, timestamps, users, and field presence without returning field values.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonNoRequired when detail_profile requests contact, content, values, or full details.
feedback_idYesLever feedback ID.
detail_profileNooperational returns an operations view; full returns the raw endpoint payload.operational
opportunity_idYesLever opportunity ID.

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the burden of disclosing behavior. It goes beyond a generic 'get' by revealing that the default output omits field values and instead includes scores, timestamps, users, and field presence. This is a valuable behavioral insight, though it does not cover error handling or authentication.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, tightly written sentence that immediately states the core purpose and a critical output trait. No filler words; every element earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description gives a key fact about default output but leaves gaps. It does not explain the 'full' detail_profile behavior, the relationship between 'reason' and detail_profile (despite schema mentioning it), or the overall response structure. For a 4-parameter tool with no output schema and no annotations, this is adequate but not complete.

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?

The input schema already provides 100% coverage, documenting each parameter with descriptions. The description does not add meaning beyond the schema; it only restates the default output behavior linked to detail_profile, which the schema already covers. Thus baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action ('Retrieve one Lever feedback form'), identifies the specific resource (feedback form), and distinguishes it from listing operations by emphasizing 'one'. This aligns perfectly with a single-get tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: when you need a single feedback form identified by feedback_id. However, it does not explicitly mention alternatives like list_opportunity_feedback for enumerating forms, so it lacks explicit exclusion guidance.

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