Generate a PNG or SVG QR code. Returns image bytes via the REST API. Output: binary image (PNG/SVG) or error JSON.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Payload to encode (URL, text, etc.) | |
| size | No | Image size in pixels | |
| format | No | png |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds useful behavioral context beyond annotations by explicitly stating that the tool returns image bytes via the REST API and outputs binary image data or error JSON. The readOnlyHint and idempotentHint are consistent with a generation operation, and there is no contradiction.
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 compact and front-loaded, conveying the core function and output type in two sentences with no filler or redundant information.
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 QR generation tool, the description covers the essential behavior and response format, while parameters are sufficiently documented in the schema. It could mention error scenarios in a bit more detail, but 'error JSON' provides adequate awareness.
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 documents the data, size, and format parameters, including defaults and the format enum. The description adds no additional parameter-level meaning, so it neither compensates for a gap nor improves on the 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 clearly states the operation ('Generate'), the resource ('QR code'), and the supported output formats ('PNG or SVG'). The verb 'Generate' also differentiates it from the sibling tool forgekit_decode_qr, which performs the inverse operation.
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 about when to use this tool versus alternatives such as forgekit_decode_qr, nor any context about appropriate use cases. The agent must infer usage from the tool name and sibling list.
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.
ship_verify and ship_verify_v2 are near-synonyms, and ship_bundle wraps ship_pack plus an audit while evidence_pack combines screenshot/PDF/HTML. The descriptions help, but an agent could easily pick the wrong granularity or version.
All tools use the forgekit_ prefix and snake_case, but verb placement is inconsistent: business_days_add/count place the noun first, evidence_pack has no verb, and the ship_* group mixes noun and verb senses. The set is readable but not uniformly patterned.
Fifteen tools is near the upper end of a reasonable utility-kit size, and most tools are individually useful. It feels slightly over-scoped because ship_verify_v2 and ship_bundle partially duplicate other capabilities rather than adding clearly new operations.
The web/media surface is broadly covered with screenshots, PDFs, QR encode/decode, OG previews, evidence packs, and agent-readiness audits. However, the set is an eclectic collection rather than one coherent workflow, and there are gaps such as no MCP-level polling for async jobs and no history/retrieval for evidence packs or ship bundles.