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

fetch_confluence_page

Read-only

Fetch a Confluence page and return its content as clean Markdown. Accepts a numeric page_id or a full page URL. Optionally lists direct child pages. BYOK — credentials transit in-memory only, never stored.

Input Schema

TableJSON Schema

Name	Required	Description
`page_id`	No	Confluence page ID (numeric string), e.g. "123456789"
`page_url`	No	Full Confluence page URL (alternative to page_id), e.g. "https://mycompany.atlassian.net/wiki/spaces/ENG/pages/123456789"
`confluence_email`	Yes	Atlassian account email (same credentials as Jira)
`confluence_token`	Yes	Atlassian API token
`include_children`	No	List direct child pages (id + title) (default: false)
`confluence_base_url`	Yes	Atlassian base URL, e.g. "https://mycompany.atlassian.net"

Output Schema

TableJSON Schema

Name	Required	Description	Default
`url`	No
`title`	No
`page_id`	No
`children`	No
`markdown`	No

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description doesn't need to repeat that. It adds valuable behavioral context: credentials transit in-memory only and are never stored, output is clean Markdown, and the tool can optionally list child pages. This exceeds the annotation baseline.

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 long, front-loaded with the core purpose, and every clause adds value: input forms, optional behavior, and credential handling. No irrelevant details or repetition.

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 that an output schema exists, the description doesn't need to detail return values. It covers the essential invocation details (page_id/URL, credentials, optional children) and auth handling. The description is complete enough for an agent to use the tool safely and 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?

The input schema has 100% coverage with detailed descriptions for every parameter, including the alternative relationship between page_id and page_url. The tool description adds little beyond restating those facts, though it does clarify 'optionality' of children listing. Since the schema is rich, the description earns the baseline score but no more.

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 uses a specific verb 'Fetch' with a clear resource 'Confluence page' and specifies the output format 'clean Markdown'. It clearly distinguishes this from sibling tools like 'create_confluence_page' and 'fetch_jira_issue' by naming the exact system and action.

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 states when to use the tool (to fetch a Confluence page) and mentions two input options (page_id or page_url) plus an optional children list. It does not explicitly name alternatives or exclusions, but the context is clear enough for an agent to select it over siblings.

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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