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

llm_output_validator

Read-onlyIdempotent

Validate an LLM response against QA criteria: format checks (JSON, code, markdown), content rules (must-include, must-not-include), length constraints, language detection, and safety patterns. Essential for QA testing LLM-powered features.

Input Schema

TableJSON Schema

Name	Required	Description
`output`	Yes	The LLM output text to validate
`max_length`	No	Maximum character length for the output
`min_length`	No	Minimum character length for the output
`check_safety`	No	Check for PII patterns (emails, phones, SSN), profanity signals, and prompt leakage
`must_include`	No	Comma-separated strings that MUST appear in the output
`expected_format`	No	Expected output format
`must_not_include`	No	Comma-separated strings that must NOT appear (e.g. "TODO, FIXME, undefined, NaN")
`check_json_schema`	No	If expected_format is JSON, provide required keys as comma-separated list to validate the structure
`expected_language`	No	Expected language of the output (en, fr, es, de…). Checks for common words.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`total`	No
`checks`	No
`failed`	No
`passed`	No
`verdict`	No

TDQS

A4/5.0

Behavior3/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description's 'Validate' is consistent. It adds a list of validation categories, but these mostly mirror the schema parameters; no additional behavioral details like auth or return behavior. 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?

Single sentence with a colon-separated list, front-loaded with verb and resource. Every clause earns its place and there is no filler or repetition of schema details.

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 9 parameters and a detailed schema, the description provides a solid high-level scope and use case. It doesn't need to explain return values because an output schema exists. A minor gap is not explicitly relating it to more specific sibling validators, but overall it is adequately complete.

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 coverage is 100% with detailed per-parameter descriptions, so baseline 3 applies. The description adds a high-level grouping of parameter categories (format, content, length, language, safety) that helps conceptualize the tool, but it doesn't add new semantics beyond the schema.

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 validates LLM responses against QA criteria, enumerating specific checks (format, content, length, language, safety). This distinguishes it from sibling tools like compare_responses or llm_format_check by positioning it as a comprehensive QA validation utility.

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?

It says 'Essential for QA testing LLM-powered features', giving clear context for when to use it. It doesn't explicitly name alternatives or exclusions, so it stops short of full usage guidance.

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.

Resources

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