RightAIChoice

Compare two AI tools head-to-head

compare_tools

Read-only

Use this when the user asks how two specific AI tools compare, which of two tools to choose, or "X vs Y". Returns live verification status for BOTH tools side by side (alive/at-risk/shut-down verdict, last-verified date, pricing reality, market sentiment) plus an editorial head-to-head verdict when a reviewed comparison exists for the pair. Data comes from the RightAIChoice verification engine: 8,000+ AI tools with every vendor link re-probed on a rolling weekly cycle, and 19,000+ maintained comparison analyses. Not for: single-tool checks (use check_tool_status), comparing more than two tools, or category browsing. If either tool is reported as not in the catalog, that is not evidence it is dead.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`tool_a`	Yes	First AI tool — product name (e.g. "Jasper") or site slug (e.g. "jasper").
`tool_b`	Yes	Second AI tool — product name or site slug.
`response_format`	No	concise = per-tool verdict + pricing + editorial verdict. detailed = adds sentiment, viability signals, and link health per tool.	concise

TDQS

A4.7/5.0

Behavior5/5

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

Goes well beyond the read-only annotations by disclosing exactly what is returned: side-by-side live verification status, last-verified dates, pricing reality, market sentiment, and a conditional editorial verdict. It also explains the data source cadence and warns that a 'not in catalog' result is not evidence a tool is dead.

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 front-loaded with the usage trigger, then covers outputs, data provenance, exclusions, and a caveat in a compact, logical order. Every sentence contributes useful information without fluff or repetition.

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?

Given there is no output schema, the description sufficiently explains return values and behaviors: the per-tool verdict, side-by-side nature, conditional editorial verdict, and the important caveat about catalog absence. It fully equips an agent to invoke 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?

The input schema already describes all three parameters with clear formats and enum values, and coverage is 100%, so the description does not need to repeat parameter details. It adds some context about what response_format might affect, but the schema carries the main semantic load.

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 states a specific verb and resource: compare exactly two specific AI tools head-to-head, with explicit trigger phrases ('X vs Y', 'which of two tools to choose'). It clearly distinguishes from siblings by emphasizing the two-tool scope and naming alternatives like check_tool_status.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use guidance (two-tool comparisons) and explicit exclusions: single-tool checks, comparing more than two tools, and category browsing. It directly names the sibling alternative check_tool_status for single-tool use, which is strong guidance.

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

A4.4/5.0

Disambiguation3/5

Most tools split cleanly by question shape (single-tool vs pair vs category vs market), but check_tool_status overlaps heavily with viability_score, market_sentiment, and pricing_reality by bundling the same evidence into one verdict. The extensive not-for cross-references help an agent route, yet several boundary cases like 'is this safe to adopt?' could reasonably go to multiple tools.

Naming Consistency3/5

The names are all readable lowercase snake_case, but they mix verb-led names (check_tool_status, compare_tools, find_alternatives, recommend_tools) with noun-phrase names (category_landscape, market_sentiment, pricing_reality, viability_score), and whats_changed follows neither pattern. There is no single consistent verb_noun convention, though the style is still predictable enough to be browsable.

Tool Count5/5

Ten tools is an appropriate size for a domain covering single-tool research, pairwise comparison, recommendations, alternatives, and aggregate market statistics. Each tool has a reasonably narrow slice of responsibility, so none feel redundant or like filler.

Completeness5/5

The set covers the key query lifecycle for AI-tool intelligence: liveness, viability, sentiment, pricing, change history, comparisons, alternatives, recommendations, category health, and market mortality. Cross-references between tools prevent dead ends, and the only notable limitations are explicitly disclosed rather than silently missing.

Resources

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