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

run_vlm_test_suite_batch

Read-only

Compare multiple VLMs on the same test suite in parallel — send an image (URL or base64) + N test cases to all models simultaneously. Returns per-model PASS/FAIL verdicts, pass rates, latency stats, and a comparison table. Assertion types: contains, not_contains, json_format, min_length, max_length, semantic_contains. BYOK: requires API keys for each provider.

Input Schema

TableJSON Schema

Name	Required	Description
`models`	Yes	Array of model IDs to compare (runs in parallel).
`api_keys`	Yes	Map of model ID → API key. Example: { "gpt-4o": "sk-...", "claude-3-5-sonnet-20241022": "sk-ant-..." }
`image_url`	No	Public URL of the image to evaluate (required unless image_base64 is provided).
`threshold`	No	Pass rate threshold for overall verdict (default: 80, 0–100).
`test_cases`	Yes	Array of test cases to run against every model.
`image_base64`	No	Base64-encoded image data (required unless image_url is provided).
`system_prompt`	No	Optional system prompt sent to every VLM.
`image_mime_type`	No	MIME type of the image if using image_base64 (default: image/jpeg).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`suites`	No
`verdict`	No
`total_failed`	No
`total_passed`	No

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable context beyond annotations: 'BYOK: requires API keys for each provider' and highlights parallel execution. It does not contradict annotations and provides extra operational detail.

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?

Three sentences, well-front-loaded with the core purpose first. Every sentence adds distinct value: what it does, what it returns, and key requirements (assertion types, API keys). No wasted words.

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 tool with 8 parameters, nested objects, and an output schema, the description covers the essential flow, output stats, and external dependency (API keys). It omits threshold defaults and more granular parameter details, but those are present in the schema. Overall adequate for an agent to invoke correctly.

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 description coverage is 100%, so the schema already documents each parameter. The description repeats some of this (image URL/base64, test cases, assertion types) but adds no new meaning beyond what's in the schema. Baseline 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 starts with a specific verb ('Compare') and clearly identifies the resource ('multiple VLMs on the same test suite') and mode ('in parallel'). It distinguishes itself from the sibling 'run_vlm_test_suite' (singular) by emphasizing multi-model batch comparison.

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?

Clear context is given: send an image plus test cases to all models simultaneously. It implies the batch use case for multi-model comparison, and the sibling tool name differentiates it from the single-model variant. However, it does not explicitly state when NOT to use it or mention alternatives.

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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