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

regex_test

Read-onlyIdempotent

Test a regular expression pattern against an input string and return all matches with their index positions and named capture groups. Use for validating user inputs, extracting structured data from text, or debugging regex patterns. Supports flags g, i, m, s, u, y. The match runs in an isolated thread with a 500 ms budget: a pattern that blows up (catastrophic backtracking, e.g. "(a+)+$") comes back as redos_detected:true — a real ReDoS verdict on your pattern — instead of hanging.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
flagsNoRegex flags: g (global), i (case-insensitive), m (multiline), s (dotAll) — default: ""
inputYesThe string to test against (max 50 KB)
patternYesRegular expression pattern (without delimiters)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNo
flagsNo
matchedNo
matchesNo
patternNo
verdictNo
elapsed_msNo
match_countNo
redos_detectedNo

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive hints. The description adds meaningful behavioral context beyond those: it runs in an isolated thread with a 500 ms budget, and catastrophic backtracking returns redos_detected:true instead of hanging. This provides a concrete safety guarantee and failure mode not encoded in annotations.

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, each earning its place: the first states the purpose and output, the second gives usage context, and the third exposes the timeout/ReDoS behavior. Information is front-loaded and there is no redundant verbiage.

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?

With an output schema present, the description need not enumerate return fields. It covers purpose, applicable use cases, flag support, input size limits (via schema), and the critical safety/failure behavior. This is complete for a moderate-complexity regex testing tool.

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

Parameters4/5

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

Schema description coverage is 100% for all three parameters, so the baseline is 3. The description adds value by listing the complete supported flag set (g, i, m, s, u, y) — the schema omits u and y — and by clarifying with an example that patterns use no delimiters. This enriches parameter understanding 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 opens with a specific verb+resource: 'Test a regular expression pattern against an input string and return all matches with their index positions and named capture groups.' This clearly differentiates the tool from sibling text-processing utilities and states its exact function.

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?

The description explicitly names three use cases: 'validating user inputs, extracting structured data from text, or debugging regex patterns.' It provides clear context for when to use the tool, though it does not mention exclusions or alternative tools. Since no sibling tool directly competes with regex testing, this is adequate.

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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