RightAIChoice

What an AI tool really costs

pricing_reality

Read-only

Use this when the user asks what a specific AI tool costs, its pricing model, plans and tiers, whether it has a free tier, hidden costs, or whether the vendor even publishes prices. Returns the recorded pricing model, per-plan prices exactly as the vendor lists them, and — unique to this catalog — documented HIDDEN COSTS: annual-commitment markups, seat minimums, paywalled features, and undisclosed usage fees. Data comes from the RightAIChoice verification engine (8,000+ AI tools re-verified on a rolling weekly cycle). The response distinguishes "the vendor publishes no prices" (their choice — sales call required) from "not recorded in our catalog" (our gap), and never converts currencies. Not for: negotiating quotes, historical price tracking, or financial advice. Prices change frequently — treat the verification date as part of the answer.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`tool`	Yes	The AI tool to get pricing for — product name (e.g. "Jasper") or site slug (e.g. "jasper").
`response_format`	No	concise = pricing model + plan prices + top 2 hidden costs. detailed = full plan list with key features and up to 6 hidden costs.	concise

TDQS

A4.8/5.0

Behavior5/5

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

Annotations declare readOnlyHint and destructiveHint, but the description adds significant behavioral context: data source (RightAIChoice engine), re-verification frequency, differentiation between vendor-no-prices and catalog-gap, and currency non-conversion. Also notes 'Prices change frequently' and verification date as part of answer, going beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is lengthy but each sentence adds distinct value: hidden costs, data source, exclusions, and verification date. It is front-loaded with the use case and clearly structured. While not terse, it avoids fluff and maintains organization.

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?

For a pricing tool with two parameters, no output schema, and rich annotations, the description provides complete context: return contents, hidden costs, distinction between no-prices and not-recorded, and verification date. It adequately covers user expectations and edge cases, making it self-sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, meeting the baseline, but the description adds meaning for response_format by explaining the exact differences (concise vs detailed outputs). The tool parameter is already descriptive in schema, but this extra detail for response_format elevates the score.

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?

Description uses specific verb ('get', 'fetch') and resource ('pricing of AI tools'), clearly distinguishing itself from siblings by highlighting hidden costs and verification engine. It explicitly states 'Use this when the user asks what a specific AI tool costs' and scopes to pricing queries.

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?

Explicitly states when to use ('when the user asks what a specific AI tool costs') and provides clear exclusions ('Not for: negotiating quotes, historical price tracking, or financial advice'). Though it doesn't name alternative tools, the exclusions and context are sufficient.

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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