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

jira_to_test_suite

Read-only

Transform a Jira ticket into a complete test suite: Gherkin scenarios, E2E steps, API test cases, test data matrix, and ambiguity detection. Accepts either Jira credentials (auto-fetch) or a pre-fetched issue object. The returned test_suite includes _gherkin_warnings (deterministic syntax validation — empty if clean). Requires BYOK LLM key (OpenAI, Anthropic, etc.).

Input Schema

TableJSON Schema

Name	Required	Description
`issue`	No	Pre-fetched issue object from fetch_jira_issue, OR a mock object with fields: key, summary, description (plain text or Markdown), status, issue_type, priority, labels, comments. Use this for offline/CI testing without Jira credentials.
`model`	Yes	LLM model to use, e.g. "gpt-4o-mini", "claude-3-5-haiku-20241022", "gemini-2.0-flash".
`api_key`	Yes	Your LLM provider API key (OpenAI sk-, Anthropic sk-ant-, Google AIzaSy-, etc.).
`issue_key`	No	Jira issue key to fetch automatically, e.g. "PROJ-123". Required if issue is not provided.
`jira_email`	No	Atlassian account email. Required for auto-fetch mode.
`jira_token`	No	Atlassian API token. Required for auto-fetch mode.
`max_tokens`	No	Maximum tokens for the LLM response. Default: 8192. Increase for large tickets with many ACs; decrease to reduce cost on simple tickets.
`jira_base_url`	No	Atlassian base URL. Required for auto-fetch mode.
`confluence_pages`	No	Optional array of pre-fetched Confluence page objects from fetch_confluence_page, used as documentation context.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`summary`	No
`issue_key`	No
`issue_url`	No
`latency_ms`	No
`model_used`	No
`test_suite`	No
`tokens_used`	No

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, so the description doesn't need to restate that. It adds valuable context: requires a BYOK LLM key, returns _gherkin_warnings with deterministic validation, and supports auto-fetch via credentials. No contradictions.

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, front-loaded with the core transformation outcome, then modes, then key requirement and output caveat. Every sentence carries distinct information with no redundancy.

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?

Given the tool's complexity (9 params, two mutually exclusive input modes, nested objects), the description covers purpose, input modes, required external key, and an important output field (_gherkin_warnings). Output schema exists, so full return values aren't needed.

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 coverage is 100%, so baseline is 3. The description adds the key relationship that credentials and pre-fetched issue are alternative modes, helping the agent decide whether to use issue or issue_key+jira_* parameters. This grouping insight goes beyond the schema's individual parameter descriptions.

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: 'Transform a Jira ticket into a complete test suite' and lists concrete outputs (Gherkin scenarios, E2E steps, API test cases, test data matrix, ambiguity detection). This clearly distinguishes it from sibling tools like generate_test_cases or prompt_test_suite by anchoring to Jira ticket input.

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 explicitly explains the two input modes: Jira credentials (auto-fetch) or a pre-fetched issue object, and the schema adds the offline/CI testing use case for the issue parameter. It does not compare against alternatives or state exclusions, so not a 5, but the context is clear.

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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