list_platformsPlatforms we track, with open role counts, median hourly pay, payout speed and availability.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| as_of | No | ||
| platforms | Yes | ||
| attribution | Yes |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "as_of": {
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "attribution": {
+ "type": "string"
+ },
+ "platforms": {
+ "items": {
+ "properties": {
+ "name": {
+ "type": "string"
+ },
+ "platform": {
+ "type": "string"
+ },
+ "url": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "platform",
+ "name",
+ "url"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "platforms",
+ "attribution"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, closed-world and non-destructive, covering the safety profile. The description adds that each platform carries role counts, median pay, payout speed and availability, but since an output schema exists this largely restates structured return data rather than disclosing new behavior such as ordering, completeness, or refresh cadence.
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?
A single compact clause with zero wasted words, and the returned metrics are front-loaded. The only weakness is the missing leading verb, which costs a little immediate clarity.
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 zero parameters, full annotation coverage and an output schema, the description needs only to establish scope, which it does. A brief note on ordering (e.g., by role count) or on whether the list is exhaustive would close 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 no parameters, so there is nothing to disambiguate and the baseline of 4 applies. The description correctly implies no filtering 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 is a noun phrase enumerating what the tool returns (platforms plus role counts, median pay, payout speed, availability), which makes the resource and its scope clear. It is distinguishable from siblings like search_jobs or get_pay_stats, though it never states an explicit verb such as 'list' or 'return'.
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?
Usage is only implied: 'Platforms we track' suggests this is the enumeration endpoint for the platform dimension, useful before filtering jobs or pay stats. There is no statement of when to call it versus the job/pay tools, and no prerequisites or 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.