RightAIChoice

Find verified-alive alternatives to an AI tool

find_alternatives

Read-only

Use this when the user asks what to use instead of a specific AI tool, for its alternatives or competitors, or for a replacement because the tool shut down, got acquired, raised prices, or is unreliable. Returns up to 3 verified-alive alternatives from the same category, each with a one-line description, current health verdict, last-verified date, and pricing model. If the asked-about tool shut down, says so (with the recorded reason) and returns live replacements. Alternatives come from the RightAIChoice verification engine (8,000+ AI tools, every vendor link re-probed on a rolling weekly cycle), ranked by category and identity-tag match — never filler from unrelated categories. The response states whether results are curated matches or a category roll-up. Not for: comparing two named tools (use compare_tools) or checking a single tool's status (use check_tool_status).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`tool`	Yes	The AI tool to find alternatives for — 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?

The description openly discloses return details (up to 3 alternatives with fields), shutdown behavior, ranking methodology, source ('RightAIChoice verification engine'), and the transparency about curated vs. roll-up results. This adds rich context beyond the readOnlyHint annotation, with no contradictions.

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 long but every sentence adds value—use case triggers, output format, edge-case behavior, ranking credibility, and exclusions. It is well-organized, with a clear 'Not for' section that closes with alternatives.

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?

With no output schema, the description fills in the gap by specifying exactly what the response contains (up to 3 alternatives, each with one-line description, health verdict, last-verified date, pricing model) and handles edge cases like shutdowns. For a tool with one parameter, this is complete.

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 fully documents the only parameter 'tool' with examples. The description adds no new parameter-level semantics, so the baseline of 3 applies. It does not repeat schema info, which is good, but offers no extra parameter guidance.

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 explicitly states the tool finds alternatives to AI tools, listing specific trigger scenarios (shutdown, acquisition, price hikes, unreliability). It distinguishes itself from siblings by name-dropping compare_tools and check_tool_status as alternatives.

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?

The description provides when-to-use guidance ('when the user asks what to use instead of a specific AI tool...') and explicit exclusions ('Not for: comparing two named tools...'). This fully satisfies the dimension.

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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