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

flatten_json

Read-onlyIdempotent

Flatten a nested JSON object to single-level dot-notation keys (e.g. {"a":{"b":1}} → {"a.b":1}), or unflatten dot-notation keys back to a nested object. Supports custom separators.

Input Schema

TableJSON Schema

Name	Required	Description
`mode`	No	"flatten" (default) or "unflatten"
`input`	Yes	The JSON to flatten or unflatten — a JSON string, or the object itself.
`separator`	No	Key separator (default: ".")

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	No
`key_count`	No
`max_depth`	No

TDQS

A4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is clear. The description adds behavioral context about directional behavior (flatten vs. unflatten) and allows custom separators, which is helpful. It does not disclose edge cases (e.g., handling arrays, collisions) or performance traits. Given strong annotations, this is acceptable but not exceptional.

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?

Two sentences cover purpose, both modes, and a key customization. Every sentence adds value with no redundancy or fluff. The example in parentheses is a nice touch that clarifies the output format without being verbose.

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?

The tool is relatively simple with 3 fully documented parameters and a clear output schema. The description covers the bidirectional transformation and customization adequately. It could mention behavior with arrays or nested objects that aren't flat, but the description and schema provide sufficient information for an agent to use 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%, so the schema already documents all three parameters. The description adds conceptual context about dot-notation transformation and separators but does not add concrete details beyond the schema. Baseline 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 flattens nested JSON to dot-notation keys and can also unflatten them back. The specific verb 'Flatten' combined with the resource 'nested JSON object' and clear one-to-many directional capability differentiates it from sibling JSON tools.

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 explains both directions (flatten/unflatten) and mentions support for custom separators, giving clear context. However, it does not explicitly state when to use this tool vs. other JSON transformation siblings like 'transform_json_array' or 'merge_json', nor does it mention when not to use it.

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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