get_salary_dataSalary benchmarks for AI/ML roles. Filter by tag (e.g. 'llm', 'pytorch'), experience level, workplace type, or company. Returns average, median, p25, p75, min, max, and sample count. Useful for compensation research and negotiation.
| Name | Required | Description | Default |
|---|---|---|---|
| tag | No | Filter by tag (e.g. 'llm', 'pytorch', 'agents') | |
| level | No | Experience level | |
| company | No | Company slug (e.g. 'anthropic', 'openai') | |
| workplace | No | Workplace type |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the output shape (average, median, p25, p75, min, max, and sample count) and implies a read-only operation via the 'get' verb. It could mention data recency or limitations, but for a simple lookup tool, this is adequate.
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 four short sentences, front-loaded with the core purpose. Each sentence adds value: scope, filters, output, and typical use case. There is no fluff or repetition.
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 description covers the tool's purpose, input filters, output metrics, and intended use case. With no output schema, it appropriately describes return values. Minor missing details like filter combinability or default behavior are not critical for tool selection.
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 schema already covers 100% of parameters with descriptions, and the tool description merely echoes the same filter concepts (tag, level, workplace, company) without adding new meaning. This aligns with the baseline of 3 for high schema coverage.
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 resource ('salary benchmarks for AI/ML roles') and the action (get/filter). It specifies available filters and the returned metrics, making it distinct from sibling tools like search_jobs or get_company.
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 mentions its usefulness for 'compensation research and negotiation', providing clear context for when to use it. However, it does not mention any alternatives or exclusions, so it lacks the explicit 'when not to use' guidance required for 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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