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canaryusers

by ai.canaryusers

canary_scan

Run a CanaryUsers UX scan on a DEPLOYED URL (your live or preview app — not source code). A flock of AI personas evaluates the page and reports where real users would get stuck, with concrete fixes. Returns AI-ready findings you can act on immediately. Use depth='deep' for the thorough scan that renders the page, checks it VISUALLY on desktop + mobile (catches mobile breakage and layout issues), and clicks through key flows like signup/checkout (slower, ~60-90s, uses one credit); depth='quick' (default) is a fast static check that does NOT see mobile or visual issues — use 'deep' when the user mentions mobile, layout, or visual problems. IMPORTANT: if this returns status 'running' with a scanId, the findings are not ready yet — wait ~30s, then call get_report_markdown(scanId), repeating until it returns the report. Always fetch and present the findings before stopping, then offer to fix the top issues.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe public URL to scan (e.g. your deployed app or preview URL).
depthNo'quick' (default, fast, free) or 'deep' (clicks through flows + visual review, ~1 credit).

TDQS

A5/5.0
Behavior5/5

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

With no annotations, the description carries full behavioral burden and excels: discloses async status behavior, credit usage for deep, visual vs. static check differences, time estimates, and the required follow-up with get_report_markdown. All beyond what the schema conveys.

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 description is long but every sentence serves a purpose: core action, depth comparison, async handling, and post-scan instructions. It is front-loaded with the main function and logically organized.

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 tool with no output schema and no annotations, the description fully covers purpose, parameters, async polling, and next steps. It even includes the agent's required action ('Always fetch and present the findings before stopping, then offer to fix the top issues'). Nothing important is missing.

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

Parameters5/5

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

Though schema covers both parameters, the description adds substantial meaning: url must be live/deployed, depth='deep' renders visually on desktop/mobile and clicks flows, uses ~1 credit, while 'quick' is a fast static check that misses those issues. This gives the agent decision-making context for selecting depth.

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?

Clearly states verb+resource: 'Run a CanaryUsers UX scan on a DEPLOYED URL' and explains the evaluation approach. Distinguishes from sibling tools by focusing on initiating a scan, while get_report_markdown retrieves reports and list_recent_scans lists past scans.

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

Usage Guidelines5/5

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

Provides explicit when-to-use guidance for depth values ('use deep when the user mentions mobile, layout, or visual problems') and describes the full workflow, including waiting for status 'running' and then calling get_report_markdown. Also instructs to always present findings and offer fixes.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool has a clearly distinct role: canary_scan initiates a new scan, get_report_markdown retrieves a specific report, and list_recent_scans lists past scans. No overlap or ambiguity exists between them.

Naming Consistency4/5

Two tools follow the verb_noun pattern (get_report_markdown, list_recent_scans), but canary_scan uses a noun_noun structure that doesn't explicitly indicate the action. The snake_case style is consistent, so the deviation is minor.

Tool Count5/5

With only 3 tools, the server is tightly scoped to the core scan workflow: create, retrieve, and list. Every tool is necessary and sufficient for the service's purpose.

Completeness5/5

The tools cover the full lifecycle of a scan: initiate, fetch the report via scanId, and list historical scans. The workflow around asynchronous scan completion is fully supported with polling via get_report_markdown.

Resources