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download_opportunity_resume

Download one Lever resume file as base64 via GET /opportunities/:opportunity/resumes/:resume/download.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonYesReason for returning a base64 file payload.
actor_idYesLever user ID associated with this action when needed.
max_bytesNo
resume_idYesLever resume ID.
opportunity_idYesLever opportunity ID.
acknowledge_sensitive_dataNoMust be true for high-sensitivity reads such as EEO PII, diversity surveys, or download URLs.

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It does mention the return format (base64) and HTTP GET, but omits critical context such as the need for actor_id, reason, and the acknowledge_sensitive_data flag for high-sensitivity downloads. The schema describes acknowledge_sensitive_data as required for sensitive data, yet the description does not surface this behavioral requirement.

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?

Single sentence, front-loaded with the action and resource, and includes the endpoint for immediate clarity. No filler words or redundancy.

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

Completeness2/5

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

Given the tool has 6 parameters, no output schema, no annotations, and sibling tools exist, the description is insufficiently complete. It does not explain the return structure beyond 'base64', nor does it indicate when to choose this download endpoint over get_opportunity_resume or list_opportunity_resumes. The sensitivity handling and actor/reason requirements are unmentioned, leaving the agent without adequate context to invoke the tool correctly in many situations.

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?

Schema description coverage is 83% (5 of 6 params documented). The description adds the URL template that implies opportunity_id and resume_id are path parameters, which aligns with schema. However, it does not elaborate on the purpose of actor_id, reason, max_bytes, or acknowledge_sensitive_data beyond what the schema already says. Baseline of 3 is appropriate given high schema coverage.

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 a specific action ('Download one Lever resume file'), the format ('base64'), and the exact endpoint path with opportunity and resume IDs. This distinguishes it from sibling tools like download_opportunity_file or get_opportunity_resume.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or which sibling tools to use instead (e.g., get_opportunity_resume for metadata). The description only states the mechanism, not the selection criteria.

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