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

number_base_convert

Read-onlyIdempotent

Convert numbers between bases: decimal, binary, octal, hexadecimal, or any base 2–36. Auto-detects 0x, 0b, 0o prefixes.

Input Schema

TableJSON Schema

Name	Required	Description
`input`	Yes	Number to convert (e.g., "255", "0xFF", "0b1010", "0o77")
`to_base`	No	Target base 2–36 (omit to get all common bases)
`from_base`	No	Source base 2–36 (auto-detects prefix if omitted)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`octal`	No
`binary`	No
`result`	No
`decimal`	No
`to_base`	No
`from_base`	No
`hexadecimal`	No

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds valuable behavioral details beyond annotations, such as auto-detecting 0x, 0b, and 0o prefixes. It does not contradict 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?

The description is one concise sentence, front-loaded with the main purpose and immediately followed by supported bases and prefix auto-detection. Every word adds value with no redundancy.

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?

Given the tool's simple nature, the presence of a rich schema, and an output schema, the description sufficiently covers the tool's core behavior. It could mention handling of invalid input, but this is not critical for a pure conversion utility.

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% and includes descriptions and examples for all parameters. The description adds no additional parameter-level detail, so the baseline score of 3 is appropriate.

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 converts numbers between bases with specific supported bases (decimal, binary, octal, hexadecimal, any base 2–36), using a specific verb and resource. It also distinguishes itself from sibling conversion tools like base64_encode/decode by specifying 'numbers' and 'base 2–36'.

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 makes the intended use clear: number base conversion. It does not explicitly name alternatives or exclusions, but the phrase 'between bases' and the listed base range provide sufficient context to differentiate from other conversion tools.

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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