Get your current salary/compensation details including base, stocks, bonus, and total compensation. IMPORTANT: For INR (currency code 2), values are in lakhs (e.g., 20 = 20 lakhs = 20,00,000 INR). For all other currencies (USD, etc.), values are in thousands (e.g., 400 = 400K = $400,000).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It clearly explains the critical unit interpretation (lakhs for INR, thousands for other currencies), which is a key behavioral trait. It does not mention auth, rate limits, or return format, but for a simple getter this disclosure is substantial.
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 concise and front-loaded: the first sentence states the purpose, and the subsequent sentences provide essential unit-conversion context. Every sentence adds value with no redundancy or fluff.
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?
Given the simplicity of the tool (no parameters) and the absence of an output schema, the description is complete enough. It states the expected content (base, stocks, bonus, total compensation) and elaborates on the important currency unit nuance, which is sufficient for an agent to understand the tool's behavior.
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 tool has 0 parameters, so the schema coverage is trivially 100%. The baseline for 0 parameters is 4, and the description adds no parameter-specific details because none exist. No additional compensation is needed.
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 retrieves current salary/compensation details including base, stocks, bonus, and total compensation. It uses a specific verb ('get') and resource ('salary/compensation'), and its purpose is distinct from sibling tools like update_salary.
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 implicitly indicates this tool is for reading salary data, and the name itself distinguishes it from update_salary. However, it does not explicitly state when to use this tool over alternatives or provide exclusions, so usage guidance is only implied.
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.
