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IA-QA — 130+ QA & Dev Tools for AI Agents

sandbox_scenario

Read-onlyIdempotent

Get a ready-made selector-drift test case with a known-correct answer, for testing this MCP server or an agent workflow end to end. Each scenario is a real DOM capture of a deliberately breakable app, taken before and after one specific UI change (a renamed label, two swapped buttons, a duplicated locator, an element moved behind a menu…), plus the verdict those two contracts MUST produce. Call with no arguments to list the scenarios; call with a scenario id to get "baseline" and "current" mappings. THE LOOP: pass baseline and current to diff_mappings, then compare its "verdict" and "counts" to this tool's "expected" — they must match exactly. A mismatch means this server's diff engine has drifted, not that your inputs are wrong. Deterministic and offline: the captures are committed fixtures, identical on every call. Try it live at https://www.ia-qa.com/devtools/sandbox

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scenarioNoScenario id. Omit to list every available scenario with its expected verdict. Ids: no-change, swap-label, add-testid, duplicate-role-name, remove-element, insert-sibling, rename-label, counter-label, move-behind-menu, add-element
include_htmlNoInclude the generated HTML of the mutated page (default false). Only useful if you want to render or re-capture it yourself; the loop does not need it.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
htmlNo
pageNo
blurbNo
countNo
titleNo
currentNo
teachesNo
baselineNo
expectedNo
scenarioNo
mutationsNo
scenariosNo
how_to_run_the_loopNo

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, so the bar is lower, but the description goes well beyond that: it reveals outputs are deterministic committed fixtures, identical on every call, offline, and that a mismatch indicates server drift rather than user error. It also clarifies the structural shape of the response (baseline/current/expected) beyond what the output schema requires.

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?

Every sentence earns its place: the purpose, the scenario content, the no-arg vs id calling convention, the integration loop, and the determinism guarantee are all packed in without redundancy. The critical workflow is highlighted with 'THE LOOP' and front-loaded before secondary details.

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 of this complexity, the description is complete: it explains what the tool returns, how to use it in combination with diff_mappings, the semantic meaning of each parameter, and the determinism/offline guarantees. The output schema and annotations cover the remaining structured details, leaving no critical gap for an agent to call it correctly.

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?

Schema coverage is 100%, giving a baseline of 3, but the description adds meaningful guidance: it explains that omitting scenario lists all scenarios, and it adds extra context for include_html ('Only useful if you want to render or re-capture it yourself; the loop does not need it'), which helps the agent decide not to set it. This exceeds schema documentation.

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 opens with a specific verb and object: "Get a ready-made selector-drift test case with a known-correct answer." It clearly identifies the resource (DOM captures of deliberately breakable apps with expected verdicts) and distinguishes it from siblings like diff_mappings by positioning itself as the fixture provider in a testing workflow.

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?

The description provides explicit usage instructions: call with no arguments to list scenarios, or with a scenario id to get baseline/current mappings. It then gives a precise multi-tool workflow ('THE LOOP') telling the agent to pass baseline and current to diff_mappings and compare its verdict/counts to the expected result, which is exactly the kind of when-to-use guidance needed.

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

A3.6/5.0
Disambiguation2/5

Multiple tools overlap significantly: compare_models/llm_fit_finder/model_info/list_llm_models all compare models; similarity_score/embedding_similarity/run_semantic_tests all measure text similarity; detect_secrets/secret_scan/analyze_diff_bugs/pr_gatekeeper all scan for secrets. Descriptions attempt to differentiate, but the boundaries between many tools are unclear, making selection error-prone.

Naming Consistency4/5

The vast majority of tools follow a snake_case verb_noun pattern (validate_email, generate_uuid, parse_csv), making the set mostly predictable. A few notable deviations exist (pr_gatekeeper, llm_fit_finder, cot_analyzer, jira_to_test_suite, needle_haystack_generate) but they are the exception rather than the rule.

Tool Count1/5

With 149 tools, this set is far beyond the 50+ threshold for an extreme mismatch. Even as a general-purpose QA & Dev toolkit, the sheer number overwhelms and exceeds any reasonable scope, making discovery and selection impractical.

Completeness4/5

The toolkit covers an impressively broad range: text processing, LLM evaluation, security auditing, web checks, MCP validation, Jira/Confluence integration, and more. Minor gaps exist, such as missing delete/update for webhooks and Confluence pages, and no create/update for Jira issues, but these are workable around.

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