api_qrQR code generator. ?data=&size=256 -> PNG as base64 in JSON. [HTTP x402 price: $0.001]
| Name | Required | Description | Default |
|---|---|---|---|
| params | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Input schema / properties / params / additionalPropertiesRemoved value: -trueInput schema / properties / params / propertiesAdded value: +{
+ "data": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null
+ },
+ "size": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null
+ }
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It does disclose the return format (PNG as base64 in JSON), the input style (?data=<text>&size=256), and the cost per call. It does not mention failure modes or limits, but for a simple generator this is reasonably transparent.
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 a single compact sentence that front-loads the core purpose and then packs in the input syntax, output format, and price. Every element earns its place.
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?
For a simple two-parameter stateless generator, the description covers the essential call information: input, output, and cost. It does not explain defaults or error handling, but the input schema and context signals already indicate that no parameters are required, and the behavior is simple enough that 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?
Schema description coverage is 0%, so the description must compensate. It shows data and size in a query-string example, implying that data is the text to encode and size is the pixel dimension, but it never explicitly defines them or their units. This adds some meaning beyond the bare schema but leaves room for ambiguity.
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 identifies the tool as a QR code generator and adds useful specifics: it accepts data and size and returns a PNG as base64 in JSON. It does not explicitly differentiate from a sibling, but the purpose is unambiguous against the sibling list.
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 gives no guidance on when to use this tool versus alternatives, no exclusions, and no context about typical use cases. It states the price and output, but an agent gets no help deciding between this and similar rendering/utility 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.