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Glama

Qrcode

by io.github.pipeworx-io

Create Qr

create_qr
Idempotent

Generate a scannable QR code from text or URLs. Returns an image URL ready to embed or download. Use when you need to encode information into a QR code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesThe text or URL to encode in the QR code.
sizeNoWidth and height of the QR code image in pixels (default 200).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesGenerated QR code image URL
dataYesThe text or URL encoded in the QR code
sizeYesWidth and height of the QR code image in pixels

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "data": {
+      "description": "The text or URL encoded in the QR code",
+      "type": "string"
+    },
+    "size": {
+      "description": "Width and height of the QR code image in pixels",
+      "type": "number"
+    },
+    "url": {
+      "description": "Generated QR code image URL",
+      "type": "string"
+    }
+  },
+  "required": [
+    "url",
+    "size",
+    "data"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "data": "https://example.com"
+  },
+  {
+    "data": "Contact: John Doe, john@example.com",
+    "size": 300
+  }
+]
  3. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already cover idempotency and non-destructive nature. The description adds that it returns an image URL ready to embed or download, which is useful behavioral context beyond the annotations. 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.

Conciseness5/5

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

Two concise sentences, front-loaded with the action. Every word earns its place, with no redundancy or filler.

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

Completeness4/5

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

For a simple tool with only two parameters and 100% schema coverage, the description covers purpose, usage, and output format. The presence of an output schema further reduces the need to explain return values in detail.

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?

Schema already provides 100% coverage for both parameters. The description confirms 'data' accepts text or URLs but adds no additional detail about the 'size' parameter beyond what the schema states. Baseline 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 generates a scannable QR code from text or URLs, distinguishing it from the sibling read_qr. The verb 'generate' and resource 'QR code' are specific and unambiguous.

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?

Provides a direct 'Use when you need to encode information into a QR code' statement, which clearly indicates the intended use case. It does not explicitly mention alternatives like read_qr, but the context is sufficient for a simple tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Qrcode toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Several tool families blur together: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions to the same underlying data sources for slightly different modes, and the six polymarket_* tools plus bet_research all orbit prediction-market opportunity-finding. The descriptions are detailed, but an agent would need to read deeply to reliably distinguish them.

Naming Consistency3/5

Names are all readable snake_case and some clusters are consistent (ask_pipeworx*, polymarket_*, pipeworx_*), but the set mixes verb-first names like create_qr and validate_claim with noun-first names like entity_profile, recent_alerts, polymarket_edges, and pipeworx_trending. There is no single predictable naming convention.

Tool Count2/5

33 tools is above the 25+ threshold and reads as a full platform rather than a focused tool. For a server labeled Qrcode, only two tools are QR-related, so the count is severely inflated even if the data-research breadth is defensible.

Completeness2/5

The Pipeworx data-research surface is fairly complete: query, grounded verification, entity profiling, comparisons, recent changes, discovery, memory, and subscriptions are all represented. But the QR domain for the stated server purpose is only create/read with no batch, styling, or management, and the overall set has no coherent domain to be complete against.

Resources