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get_opportunity_note

Retrieve one Lever opportunity note. Default output shows note metadata; full note values require detail_profile=full with a reason.

Input Schema

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

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses two important behaviors: the default output is metadata-only, and full note values require detail_profile=full with a reason. This goes beyond the schema by explaining a conditional requirement. It does not cover auth, rate limits, or error cases, but for a read tool the key behavioral nuance is addressed.

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?

Two sentences, no filler. The first sentence is a clean action statement. The second sentence provides essential behavioral detail (default vs full, reason requirement). Every word earns its place, and the structure front-loads the primary purpose.

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

Completeness4/5

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

For a tool with no output schema and no annotations, the description covers the core purpose and the most important edge behavior (full vs metadata). It omits what exactly constitutes 'metadata' vs 'full values,' and doesn't describe response format, but the default/full distinction gives enough guidance. This is complete for most agent interactions.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, providing baseline 3. The description adds semantic meaning by linking detail_profile=full to the reason requirement, which is not captured in the schema (reason is simply optional with length constraints). This clarifies the inter-parameter dependency and raises the score to 4.

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 opens with 'Retrieve one Lever opportunity note,' using a specific verb ('Retrieve') and clear resource ('Lever opportunity note'). It unambiguously distinguishes this from sibling tools like list_opportunity_notes (which lists multiple) and update_opportunity_note/delete_opportunity_note. The scope is singular and precise.

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 gives clear context on when to use the 'full' detail_profile versus the default, and notes that 'full' requires a reason. However, it does not explicitly name or exclude alternative tools (e.g., list_opportunity_notes for retrieving multiple notes). The guidance is implied rather than explicit, meriting a 4 rather than a 5.

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