get_pay_statsMarket pay for AI training work (median and quartiles of hourly rates stated in listings, by platform) and hiring outcomes from the latest monthly AI Training Pay Index.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| as_of | No | ||
| market | Yes | ||
| pay_index | No | ||
| attribution | Yes | ||
| methodology | Yes |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "as_of": {
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "attribution": {
+ "type": "string"
+ },
+ "market": {
+ "type": "object"
+ },
+ "methodology": {
+ "type": "string"
+ },
+ "pay_index": {
+ "type": [
+ "object",
+ "null"
+ ]
+ }
+ },
+ "required": [
+ "market",
+ "methodology",
+ "attribution"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, closed-world, and non-destructive behavior, so the safety profile is covered. The description adds useful context beyond that: the data comes from hourly rates stated in listings, is broken down by platform, and reflects the latest monthly index — giving the agent freshness and provenance cues. It stops short of noting update cadence details or staleness caveats.
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?
One sentence, zero filler, with the resource and its two components (pay distribution and hiring outcomes) front-loaded. Nothing needs to be trimmed or reordered.
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?
With an output schema present and no parameters, the description only needs to convey what data is returned and its source, which it does. A brief note on what the hiring-outcomes component comprises would have closed the remaining 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 tool takes zero parameters, so the baseline is 4. The description correctly implies a single unfiltered call covering all platforms, which is consistent with the empty schema.
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 names the resource precisely: market pay statistics (median and quartiles of hourly rates from listings, by platform) plus hiring outcomes from the AI Training Pay Index. This clearly separates it from siblings like search_jobs, list_platforms, or get_interview_prep, though it leads with a noun phrase rather than a verb such as 'get' or 'returns'.
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?
There is no guidance on when to call this tool versus alternatives — nothing like 'use this for aggregate pay benchmarks rather than individual listings'. An agent must infer the use case entirely from the content description, and no conditions or exclusions are stated.
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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