Get aggregated stats for your job applications — total counts by status and auto-apply metrics. Much faster than paginating through list_applications.
| Name | Required | Description | Default |
|---|---|---|---|
| jobHuntId | No | Filter stats to a specific job hunt | |
| dateOffset | No | Filter by time period (e.g., "24H", "7D", "1M", "3M", "1Y") |
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 explains the tool produces aggregated stats and is faster than list_applications, which is useful context. However, it does not describe the exact output structure, whether stats include all applications or only those matching optional filters, potential rate limits, or any freshness/real-time behavior.
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 extremely concise—one sentence with two clauses. It front-loads the core purpose ('Get aggregated stats') and immediately adds distinguishing value ('faster than paginating'). No filler or redundant words.
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?
Despite lacking an output schema, the description effectively conveys the main return information (counts by status and auto-apply metrics) and the performance advantage. With only two optional parameters and a straightforward aggregation use case, the description is largely complete. It could benefit from stating whether stats are real-time or cached, but this is a minor gap.
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?
The input schema provides 100% description coverage for both parameters (jobHuntId and dateOffset), so the description adds no necessary parameter-level semantics. The baseline of 3 applies because the schema fully documents the parameters; the description's mention of 'auto-apply metrics' hints at what is aggregated but does not extend parameter meaning.
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 clearly states the tool's function: 'Get aggregated stats for your job applications' with specific detail on what it returns ('total counts by status and auto-apply metrics'). It also distinguishes itself from sibling tool list_applications by emphasizing aggregation rather than raw paginated data.
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 explicitly positions this tool against an alternative: 'Much faster than paginating through list_applications.' This gives clear guidance to use this tool when aggregate stats are needed rather than paginating through individual records, directly addressing selection between sibling tools.
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.
