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

http_status_lookup

Read-onlyIdempotent

Look up detailed information about any HTTP status code: class, name, description, cacheability, typical causes, and handling best practices. Covers every code in the IANA HTTP Status Code Registry (1xx-5xx, including 226, 425, 451, 508, 511 and the WebDAV codes) with its defining RFC; anything outside the registry is reported as registered: false rather than described. cacheable means heuristically cacheable by default per RFC 9110 §15.1.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesHTTP status code (e.g. 200, 404, 429, 503)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeNo
descNo
nameNo
classNo
cacheableNo
registeredNo
descriptionNo

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly and idempotent, which the description reinforces with a clear non-destructive lookup nature. The description adds value by specifying the registry scope and reporting behavior for non-registered codes (registered: false), and clarifies the meaning of cacheability per RFC 9110. However, it doesn't mention response format details like pagination or error codes, but given the annotations and output schema, the marginal gap is acceptable.

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 compact, two sentences, and conveys all necessary details: purpose, scope, edge cases, and specific terminology. Zero fluff, front-loaded with the primary function.

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 simple (one parameter, output schema present, no nested objects). The description covers the registry scope, non-registry behavior, and the meaning of cacheability. It might benefit from mentioning the output format or the returning of RFC references, but the output schema presumably covers that. Not enough to score 5, but solidly complete for the tool's complexity.

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% (code parameter fully described). The description does not add extra syntax or formatting details beyond what the schema provides, but the schema itself is sufficient given the single, self-explanatory parameter. 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 uses a specific verb ('Look up') with a clear resource ('HTTP status code') and specifies the exact scope (IANA registry, 1xx-5xx including edge codes). It clearly distinguishes from sibling tools which are mostly unrelated (encoding, formatting, analysis of other domains).

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 clearly states the tool is for looking up detailed info about HTTP status codes, covering the entire IANA registry. It does not explicitly mention alternative tools or when not to use it, but the broad coverage and domain specificity effectively imply usage context, and it explicitly notes the behavior for non-registry codes.

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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