RightAIChoice

How likely an AI tool is to survive

viability_score

Read-only

Use this when the user asks whether a specific AI tool is safe to adopt or build on, likely to still exist next year, well-maintained, gaining or losing momentum — or asks about its long-term viability or abandonment risk. Returns a viability BAND (safe bet / moderate / at risk) with the five measured drivers behind it: what the vendor publishes, site health, traction, user sentiment, and recent activity — plus which signal is weakest and why, in plain language. Assessments come from the RightAIChoice verification engine (8,000+ AI tools, every vendor link re-probed on a rolling weekly cycle). A null driver means "not yet measured", which is different from a bad score. Bands are NOT a leaderboard: they classify risk, they do not rank tools against each other. Not for: comparing two tools (use compare_tools), liveness checks alone (use check_tool_status), or investment decisions.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`tool`	Yes	The AI tool to assess — product name (e.g. "Jasper") or site slug (e.g. "jasper").

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true, openWorldHint=false, destructiveHint=false, so the safety profile is known. The description adds substantial behavioral context: it returns a viability band with five measured drivers, explains that null means 'not yet measured' (distinct from a bad score), and clarifies that bands are risk classifications, not rankings. It also mentions the rolling weekly re-probing of vendor links, giving transparency into data freshness.

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 information-dense but well-structured: use cases, return value, methodology, null semantics, and exclusions are each clearly separated. Every sentence adds a distinct piece of information – no fluff, no repetition of schema or annotations. It is appropriately sized for the tool's complexity.

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?

The tool has no output schema, so the description carries the burden of explaining return values. It clearly describes the output format (viability band, drivers, weakest signal) and interprets the 'null driver' case. It also notes the scale of the underlying engine (8,000+ tools) and the re-probing cycle. Given the tool's analytical nature, this is a complete and self-contained description.

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 schema already documents the single parameter 'tool' with a description that includes examples and field format. Maximum length and required status are also in the schema. Since schema description coverage is 100%, the baseline is 3. The tool description does not add further parameter-specific nuances beyond what the schema provides, so no higher score is warranted.

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 opens with specific use cases: assessing safety to adopt, longevity, maintenance, momentum, and abandonment risk. It clearly distinguishes itself from siblings by naming compare_tools and check_tool_status as alternatives for other needs. The verb 'use this when' and concrete risk questions make the purpose unmistakable.

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?

Explicit 'when to use' scenarios are listed in the first sentence, followed by an explicit 'Not for' section that names two alternative tools. This directly answers when to use this tool versus alternatives, providing clear decision criteria for the agent.

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.

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