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

create_confluence_page

Create a new Confluence page from the output of jira_to_test_suite. Formats Gherkin, E2E steps, API tests, and test data as a properly structured Confluence page with code blocks and tables. STATEFUL — creates a new page in the specified space.

Input Schema

TableJSON Schema

Name	Required	Description
`title`	No	Page title. Defaults to "Test Plan: {issue_key}"
`issue_key`	No	Source Jira issue key (for the page title and source link)
`issue_url`	No	Source Jira issue URL (added as a link in the page)
`space_key`	Yes	Confluence space key where the page will be created, e.g. "QA", "ENG"
`test_suite`	Yes	The test_suite object from jira_to_test_suite result
`parent_page_id`	No	Optional parent page ID — page will be created as a child of this page
`confluence_email`	Yes	Atlassian account email
`confluence_token`	Yes	Atlassian API token
`confluence_base_url`	Yes	Atlassian base URL

Output Schema

TableJSON Schema

Name	Required	Description	Default
`title`	No
`page_id`	No
`success`	No
`page_url`	No

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already establish state-changing behavior via readOnlyHint=false and idempotentHint=false. The description adds value by disclosing that the page is structured with code blocks and tables, and reiterates the stateful nature. This goes beyond what annotations provide, although no additional warnings about duplicates or auth are included.

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?

The description is two sentences, front-loaded with the core action and source, followed by formatting and statefulness. Every word earns its place; no redundancy or filler.

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 parameters, nested objects, output schema present), the description provides essential workflow context, source relationship, and formatting expectations. The output schema covers return details, so the description is sufficient for an agent to select and invoke the tool 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%, with each parameter already having a meaningful description (e.g., test_suite defined as 'from jira_to_test_suite result'). The tool description does not add new parameter details beyond the schema, so it meets the baseline but doesn't exceed it.

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 action ('Create a new Confluence page') and the specific source ('output of jira_to_test_suite'), distinguishing it from sibling tools like fetch_confluence_page. It also details the content formatting (Gherkin, E2E steps, API tests) making the purpose unambiguous.

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 implies when to use it: after jira_to_test_suite produces its output. It does not explicitly mention alternatives or exclusions, but the 'from the output of jira_to_test_suite' phrase provides clear context. The STATEFUL warning adds a caution about side effects, which is useful for decision-making.

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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