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

normalize_whitespace

Read-onlyIdempotent

Normalize whitespace: trim trailing spaces, collapse blank lines, normalize line endings (LF/CRLF), convert tabs to spaces. Useful for cleaning code, configs, and text before processing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesText to normalize
trim_fileNoTrim leading/trailing blank lines (default: true)
trim_linesNoTrim trailing whitespace from each line (default: true)
line_endingNo"lf" (default), "crlf", or "cr"
tab_to_spacesNoConvert tabs to N spaces (omit to keep tabs)
collapse_blanksNoCollapse runs of blank lines down to max_blank_lines (default: true)
max_blank_linesNoBlank lines to keep when collapsing, 0-10 (default: 2)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNo
line_endingNo
original_lengthNo
normalized_lengthNo

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, and the description adds concrete transformation behavior. However, it states 'convert tabs to spaces' as a flat fact while the schema shows tab_to_spaces is optional and defaults to keeping tabs. This could mislead an agent about the default behavior, creating a transparency gap.

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 with dense, front-loaded information. The first sentence enumerates all operations in a compact list; the second gives a practical use case. There is no filler, restating of the tool name, or redundant content.

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 rich annotations, 100% parameter schema coverage, and presence of an output schema, the description covers the tool's main behaviors and usage contexts sufficiently. The only gap is the tab conversion default ambiguity, which prevents a perfect completeness score.

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%, so the baseline is 3. The description's transformation list paraphrases parameters (trim_lines, collapse_blanks, line_ending, tab_to_spaces) but provides no additional default, format, or range details beyond what the schema already documents. It adds marginal semantic value.

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 clear verb and resource, 'Normalize whitespace,' followed by a precise enumeration of transformations ('trim trailing spaces, collapse blank lines, normalize line endings (LF/CRLF), convert tabs to spaces'). This distinguishes it from sibling tools like sort_lines or format_json by specifying exactly what whitespace normalization does.

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 second sentence offers clear usage context: 'Useful for cleaning code, configs, and text before processing.' It tells the agent when to use it, but does not explicitly name alternatives or exclusion cases, so it falls short of the 5-level despite being a clear and practical guideline.

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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