read_qrDecode QR code images to extract embedded text or URLs. Returns the decoded content. Use when you need to read what's stored in a QR code.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Publicly accessible URL of the QR code image to decode. |
| Name | Required | Description | Default |
|---|---|---|---|
| decoded | Yes | The decoded text or URL extracted from the QR code |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "decoded": {
+ "description": "The decoded text or URL extracted from the QR code",
+ "type": "string"
+ }
+ },
+ "required": [
+ "decoded"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "url": "https://example.com/qrcode.png"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a safe, read-only, idempotent operation. The description adds that it returns decoded content and extracts text/URLs, which complements the safety profile without contradicting the annotations.
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 brief and front-loaded with the action, but there is slight redundancy across the three sentences (extract, returns, read stored). Still, it is appropriately sized and every sentence adds some value.
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 a single well-documented parameter, an output schema, and clear annotations, the description provides all necessary context for this simple tool. It covers the action, return value, and use case, leaving no major gaps.
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 single parameter 'url' is already well-described in the schema as a publicly accessible URL to a QR code image. The description does not add further details about parameters, so it relies on the schema's high coverage.
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 tool decodes QR code images to extract embedded text or URLs. It distinguishes itself from the sibling tool create_qr by explicitly focusing on reading/decoding rather than generating.
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?
It explicitly says to use when you need to read what's stored in a QR code, giving clear context. However, it does not mention alternatives or when not to use, which for a simple tool is acceptable but not fully exhaustive.
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.
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.
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.
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.
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.