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

score_geo_signals

Read-onlyIdempotent

Analyze a webpage HTML (or full HTML) for GEO (Generative Engine Optimization) signals. Returns a score /60 with per-check results and improvement tips. GEO = optimizing pages for AI-powered search engines (ChatGPT Search, Perplexity, etc.).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`head_html`	Yes	Raw HTML of the <head> section (or full page HTML) to analyze

Output Schema

TableJSON Schema

Name	Required	Description	Default
`grade`	No
`score`	No
`checks`	No
`passed`	No
`max_score`	No
`total_checks`	No

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already indicate read-only, non-destructive, and idempotent behavior. The description adds meaningful behavioral context beyond annotations by stating the output format ('score /60'), that it includes 'per-check results and improvement tips,' and that it accepts either head or full HTML. No contradictions exist between description and 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 two sentences with no fluff: the first states the action and input, the second explains the output format and defines the GEO acronym. Every sentence earns its place, and the structure is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a low-complexity tool with one parameter and an output schema, so the description sufficiently covers what, why, and expected output. It mentions the scoring scale, per-check results, tips, and input flexibility, leaving no critical gap for successful invocation.

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 single parameter is fully documented in the schema ('Raw HTML of the <head> section (or full page HTML) to analyze'), so the description need not repeat it. The tool description reinforces the input flexibility but adds no new parameter-level information beyond the schema. Baseline 3 applies due to high schema description coverage.

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 ('Analyze') and resource ('webpage <head> HTML or full HTML') and clearly states the tool's purpose: detecting GEO signals for AI-powered search engines. It differentiates itself from sibling analysis tools by naming the unique GEO scoring focus and expanding the acronym.

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 implies when to use the tool: whenever you need to evaluate a page's GEO readiness for AI search engines. It does not explicitly name alternatives or exclusions, but the clear purpose and context ('optimizing pages for AI-powered search engines') provide strong guidance. The lack of explicit 'use this instead of X' prevents a 5.

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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