List AI-generated custom resumes. These are resumes that were automatically tailored for specific job applications.
| Name | Required | Description | Default |
|---|---|---|---|
| manualTrigger | No | Filter by whether resume was manually triggered | |
| jobApplicationId | No | Filter by job application ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only restates the domain and does not mention pagination, return format, ordering, authentication, or any side effects. The read-only nature is inferred from 'List' but not explicitly disclosed.
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 with no filler. The first sentence is a clear front-loaded command, and the second sentence adds useful context about the resume type without redundancy.
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 is low complexity: two optional filter parameters fully documented in the schema, no output schema, and no nested objects. The description sufficiently explains the core purpose and domain, though it lacks any behavioral return details. Since the task is a simple list, this feels complete enough.
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%, with both parameters ('manualTrigger' and 'jobApplicationId') self-explanatory in the schema. The description adds no parameter-level detail, but the schema fully covers semantics, so a baseline score of 3 is appropriate.
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+resource combo: 'List AI-generated custom resumes.' It clearly distinguishes this tool from the sibling 'list_resumes' by adding 'AI-generated custom' and elaborates with 'automatically tailored for specific job applications.'
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 usage by defining what AI-generated custom resumes are, which helps differentiate from list_resumes, but it does not explicitly provide when-to-use guidance or name alternatives. There is no direct comparison to sibling tools or mention of exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource and action. Tools like get_job vs get_application vs get_job_hunt are clearly separated, and match_jobs vs search_jobs are well-differentiated by saved vs explicit filters. No two tools appear to do the same thing.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., create_job_hunt, list_applications, update_salary). Even longer names like add_job_to_applications maintain the convention with clear, predictable structure.
With 35 tools, the server exceeds the 25+ threshold that indicates an overly large surface. While the breadth covers a comprehensive job search workflow, the number is likely overwhelming and could be consolidated without losing functionality.
The tool set covers the full job hunt lifecycle: creating hunts, searching/matching jobs, applying, tracking applications, managing resumes (including AI-generated versions), outreach, interviews, profile, and compensation. There are no obvious dead ends; update and delete operations are available where needed.
