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

sort_lines

Read-onlyIdempotent

Sort, deduplicate, reverse, or filter lines of text. Useful for cleaning import lists, dependencies, log files, and config entries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
trimNoTrim whitespace from each line (default: true)
inputYesMulti-line text to process
filterNoFor "filter": keep lines containing this substring (case-insensitive)
operationNo"sort" (default), "sort_desc", "reverse", "deduplicate", "unique_sort", "filter"
remove_emptyNoRemove empty lines (default: true)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNo
removedNo
line_countNo
original_countNo

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds value by explicitly listing the transformations performed (sort, deduplicate, reverse, filter) and the typical target data (import lists, dependencies, etc.). This extra context helps the agent understand the operational behavior without relying solely on the parameter schema.

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 a clear verb phrase, and contains no filler. The second sentence adds practical usage context without redundancy. Every word earns its place, making it an exemplar of concise tool description.

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 moderate complexity (5 parameters, multiple operations) and the availability of both a 100% explanatory schema and an output schema, the description is adequately complete. It communicates the core purpose and common use cases, while the schema handles operational details. It could optionally mention default behaviors (e.g., trim defaults to true), but the schema already covers these, so the description does not need to repeat them.

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 for all five parameters, including a detailed description for the 'operation' parameter listing all possible values. The description's mention of 'sort, deduplicate, reverse, or filter' aligns with the schema but does not introduce new parameter-specific details, so a 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 identifies the tool's function with a specific verb phrase: 'Sort, deduplicate, reverse, or filter lines of text.' This distinguishes it from sibling text utilities by explicitly enumerating distinct operations and naming the target resource (lines of text). It goes beyond a simple tautology and fully captures the tool's scope.

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 provides clear usage context by stating it is 'useful for cleaning import lists, dependencies, log files, and config entries.' This gives the agent a sense of when to select this tool, though it does not explicitly mention when not to use it or alternative tools. The absence of explicit exclusions is a minor gap, but the use-case list is sufficient for most selection scenarios.

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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