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agishub

AgisHub MCP Server

Official
by agishub

qr_code

Generate QR codes from text or URLs, returning an inline SVG and data URI. Options include size, quiet-zone margin, and error-correction level.

Instructions

Generate a QR code for any text or URL. Returns an inline SVG plus a data URI, with selectable size, quiet-zone margin and error-correction level.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoPixel size of each QR module/cell in the SVG (default 6).
textYesText or URL to encode in the QR code.
marginNoQuiet-zone margin around the code, in cells (default 4).
ec_levelNoError-correction level: L(7%), M(15%), Q(25%), H(30%). Default M.
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of stating behavior. It discloses the return type ('inline SVG plus a data URI') and mentions adjustable options (size, margin, error-correction). It does not mention any side effects, which is acceptable for a pure generation tool, but slightly more detail (e.g., the data URI format) would improve transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The entire description is a single, information-dense sentence. It leads with the action and output, then lists the configurable aspects. Every phrase adds value, with no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a straightforward generation tool, the description covers the key aspects: purpose, input type, output format, and available options. Since the schema exhaustively documents parameters and there is no output schema to explain, this is fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All four parameters are fully described in the input schema, so the schema coverage is 100%. The description mentions the parameters in natural language ('selectable size, quiet-zone margin and error-correction level') but does not add new semantic information beyond what the schema already provides. Thus, the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Generate a QR code for any text or URL.' It uses a specific verb (Generate) and identifies the resource (QR code), with no ambiguity. It also specifies the output format, making it easy to distinguish from any even vaguely related tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the use case: whenever a QR code for text/URL is needed. There are no sibling tools in the list that generate QR codes, so no explicit alternatives are required. It doesn't provide exclusions, but the context is clear enough for selection.

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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