ai-apps-that-work-tools
Registry code: f7829c4e36bb5f34
Read-only tools for people building AI apps: real cost per AI result, model prices, the cost of switching models, announced model shutdowns, A/B test sample sizes, LLM-judge agreement, judge prompts and an AI launch checklist. Model prices come from the providers' own pages and were checked on 2026-11-14. Every result includes a permalink to a page on https://aiappsthatwork.com that shows the same result. Inputs are computed and not stored.
- endpoint
- https://aiappsthatwork.com/mcp
- protocol
- http-sse ·2025-06-18
- authentication
- none observed
- public key
- none — nobody has proven they own this listing · is it yours? claim it
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- 0 · newcomer
- Is ai-apps-that-work-tools live?
- Yes — it answered the hub's last check (checked 1h ago). It answered 100% of checks over the last 30 days.
- Is ai-apps-that-work-tools free to use?
- Yes — the hub reached it with no key and no payment.
- What tools does ai-apps-that-work-tools have?
- 8 tools: build_judge_prompt, real_cost_per_result, model_switch_cost, model_shutdowns, ab_test_sample_size, judge_agreement, launch_checklist, model_prices.
- Is ai-apps-that-work-tools safe to connect?
- The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.
90 days 100%· all time 100%
last good check
of 8 tools
- unknown → live
Calls placed through this hub's router, from its own receipts. Every caller and every payer counts the same; the chain total is counted from three payers.
through this hub
successful
what callers paid
Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.
launch_checklist open 1h ago
The 20 AI-specific launch checks for an AI app (spending caps, rate limits, server-side keys, credit charging, version tags, records of which results worked first time, real cost per result, switching models safely). Returns the checks not yet done, each with why it matters and a request for a coding agent, plus one combined request.
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "properties": { "done": { "type": "array", "items": { "type": "string", "maxLength": 60 }, "default": [], "maxItems": 30, "description": "Ids of checks already done. Ids: spend-cap, rate-limits, server-keys, capped-retries, provider-budget, every-call-charged, deduct-server-side, refund-failures, one-charge-per-request, output-metadata, versioned-regenerations, outcome-events, events-by-version, step-costs, wasted-spend, who-it-fails-for, prompts-in-git, switch-flags, replay-set, shutdown-plan." }, "group": { "enum": [ "cost", "charging", "traceability", "signals", "change" ], "type": "string", "description": "Only checks in one group: cost (cost and abuse), charging (charging for ai use), traceability (a record of every ai result), signals (what worked, for whom, at what cost), change (switching safely)" } } }arguments 27 linesmodel_prices open 1h ago
Looks up list prices for LLMs (USD per million input, output and cached input tokens, and context size), image models (USD per image by quality and size) and video models (USD per second by resolution), with each price's source page, the date prices were checked and any announced API shutdown.
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "properties": { "kind": { "enum": [ "llm", "image", "video", "all" ], "type": "string", "default": "all", "description": "llm for text models priced per token, image per image, video per second" }, "limit": { "type": "integer", "default": 25, "maximum": 100, "minimum": 1, "description": "Most models to return" }, "query": { "type": "string", "maxLength": 100, "description": "A model id such as 'gpt-4o' (only exact id matches, if any), or words that must all appear in the id, name or provider, such as 'claude sonnet'. Omitted: every model." } } }arguments 29 linesbuild_judge_prompt unknown never probed
Builds a pass/fail LLM-as-a-judge prompt for one kind of AI step (make, answer, transform, extract, judge, find, predict, plan, act or learn). Returns the prompt with {{input}} and {{result}} placeholders, numbered pass conditions (the block's starter conditions unless you send your own), up to 3 pass and 3 fail examples interleaved, and a JSON or XML answer format.
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "required": [ "block" ], "properties": { "block": { "enum": [ "make", "answer", "transform", "extract", "judge", "find", "predict", "plan", "act", "learn" ], "type": "string", "description": "What the AI step does. make: creates new content from a request (an image, a post, slides, advice); answer: replies to a question that has a right answer (a support reply, a policy question, a how-to); transform: reshapes material faithfully (a rewrite, a translation, text to speech); extract: pulls out what's in the input (fields from a PDF, speech to text, text from a photo); judge: gives a label, score or choice that can be checked now (moderation, routing, a guardrail); find: picks the few relevant items out of many (search, recommendations); predict: makes an estimate that can only be checked later (will this lead buy, is this fraud, when will it arrive); plan: turns a goal into steps (a research plan, a multi-step task); act: takes one action with side effects (send an email, take a payment, call an API); learn: updates a model or prompt from examples (a voice clone, prompt tuning)." }, "criteria": { "type": "array", "items": { "type": "string", "maxLength": 500, "minLength": 1 }, "maxItems": 10, "minItems": 1, "description": "Pass conditions, one per item. Omitted: the starter conditions for the block." }, "outputFormat": { "enum": [ "json", "xml-tags" ], "type": "string", "default": "json", "description": "How the judge answers" }, "failedExamples": { "type": "array", "items": { "type": "object", "required": [ "result" ], "properties": { "input": { "type": "string", "default": "", "maxLength": 4000, "description": "What the step received" }, "result": { "type": "string", "maxLength": 4000, "description": "What the step produced, in full" } } }, "default": [], "maxItems": 3, "description": "Real results that didn't work; labelled fail in the prompt" }, "workedExamples": { "type": "array", "items": { "type": "object", "required": [ "result" ], "properties": { "input": { "type": "string", "default": "", "maxLength": 4000, "description": "What the step received" }, "result": { "type": "string", "maxLength": 4000, "description": "What the step produced, in full" } } }, "default": [], "maxItems": 3, "description": "Real results that worked first time; labelled pass in the prompt" }, "includeReasoning": { "type": "boolean", "default": true, "description": "Whether the judge writes a short reason before its verdict" }, "featureDescription": { "type": "string", "default": "", "maxLength": 2000, "description": "Who uses the feature, what they ask for and what a good result gives them" } } }arguments 106 linesreal_cost_per_result unknown never probed
Prices the AI steps behind one request from current list prices (LLM tokens, images, video seconds or a fixed amount) and returns the cost per request, the real cost per result that worked first time (cost per request divided by the worked-first-time share), the monthly spend wasted on results that didn't work, and credit-pricing numbers: margin per result and the credits per request needed to break even or reach a target margin.
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "required": [ "steps", "workedFirstTimePct", "requestsPerMonth" ], "properties": { "steps": { "type": "array", "items": { "oneOf": [ { "type": "object", "required": [ "kind", "model", "inputTokens", "outputTokens" ], "properties": { "kind": { "type": "string", "const": "llm" }, "note": { "type": "string", "maxLength": 120, "description": "What the step does, for your own reference" }, "model": { "type": "string", "minLength": 1, "description": "LLM model id from /models.json, for example anthropic/claude-haiku-5.5" }, "inputTokens": { "type": "integer", "maximum": 10000000, "minimum": 0, "description": "Input tokens per request" }, "outputTokens": { "type": "integer", "maximum": 10000000, "minimum": 0, "description": "Output tokens per request" }, "cachedInputPct": { "type": "number", "default": 0, "maximum": 100, "minimum": 0, "description": "Share of input tokens billed at the provider's cached-input price, 0 to 100" } } }, { "type": "object", "required": [ "kind", "model", "variant" ], "properties": { "kind": { "type": "string", "const": "image" }, "note": { "type": "string", "maxLength": 120, "description": "What the step does, for your own reference" }, "model": { "type": "string", "minLength": 1, "description": "Image model id from /models.json, for example google/nano-banana-2.1" }, "images": { "type": "integer", "default": 1, "maximum": 100, "minimum": 1, "description": "Images per request" }, "variant": { "type": "string", "minLength": 1, "description": "Quality and size label exactly as listed for the model in /models.json" } } }, { "type": "object", "required": [ "kind", "model", "variant", "seconds" ], "properties": { "kind": { "type": "string", "const": "video" }, "note": { "type": "string", "maxLength": 120, "description": "What the step does, for your own reference" }, "model": { "type": "string", "minLength": 1, "description": "Video model id from /models.json, for example google/veo-3.1-fast" }, "seconds": { "type": "number", "maximum": 600, "description": "Seconds of video per request", "exclusiveMinimum": 0 }, "variant": { "type": "string", "minLength": 1, "description": "Resolution and audio label exactly as listed for the model in /models.json" } } }, { "type": "object", "required": [ "kind", "usd" ], "properties": { "usd": { "type": "number", "maximum": 1000, "minimum": 0, "description": "What this step costs per request, in USD" }, "kind": { "type": "string", "const": "custom" }, "label": { "type": "string", "maxLength": 120, "description": "What the step is, for example a speech-to-text API" } } } ] }, "maxItems": 4, "minItems": 1, "description": "The AI steps behind one request, priced from /models.json. Their costs add up to the cost per request." }, "pricing": { "type": "object", "default": {}, "properties": { "feePct": { "type": "number", "default": 2.9, "minimum": 0, "description": "Payment processor fee, percent of the pack price", "exclusiveMaximum": 100 }, "feeFixedUsd": { "type": "number", "default": 0.3, "minimum": 0, "description": "Payment processor fixed fee per pack, USD" }, "packCredits": { "type": "number", "default": 1000, "description": "Credits in that pack", "exclusiveMinimum": 0 }, "packPriceUsd": { "type": "number", "default": 10, "description": "What a user pays for one credit pack, USD", "exclusiveMinimum": 0 }, "targetMarginPct": { "type": "number", "default": 70, "minimum": 0, "description": "Gross margin wanted on each result that worked, percent", "exclusiveMaximum": 100 }, "chargeOnlyWorked": { "type": "boolean", "default": false, "description": "True if users are charged only for results that worked" }, "creditsPerRequest": { "type": "number", "default": 1, "description": "Credits charged each time the AI runs", "exclusiveMinimum": 0 } }, "description": "Credit pricing. Defaults: $10 for 1,000 credits, 1 credit per request, a 2.9% + $0.30 fee and a 70% target margin." }, "requestsPerMonth": { "type": "number", "maximum": 1000000000, "minimum": 0, "description": "Requests per month, try-agains included" }, "workedFirstTimePct": { "type": "number", "maximum": 100, "minimum": 1, "description": "Share of results that worked first time: no try again, undo, fix, re-ask or overrule. 1 to 100." } } }arguments 223 linesmodel_switch_cost unknown never probed
Compares two priced LLMs on six common task shapes (support answer, invoice extraction, lead scoring, email draft, translation, search re-ranking). Returns list prices, cost per 1,000 requests on each model, the cost ratio, the break-even (how often the new model's results must work first time, relative to the current one, to cost no more per result that works) and the current model's announced shutdown.
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "required": [ "from", "to" ], "properties": { "to": { "type": "string", "maxLength": 100, "minLength": 1, "description": "The new model: an LLM id from /models.json (openai/gpt-6-luna), an OpenRouter id or the model's name" }, "from": { "type": "string", "maxLength": 100, "minLength": 1, "description": "The current model: an LLM id from /models.json (openai/gpt-4o), an OpenRouter id or the model's name" } } }arguments 22 linesmodel_shutdowns unknown never probed
Lists announced API shutdown dates for AI models from OpenAI, Anthropic, Google and other providers, with days left, the suggested replacement, the source page, and links to a cost comparison and a side-by-side test of the replacement where this site has them.
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "properties": { "query": { "type": "string", "maxLength": 100, "description": "A model id such as 'gpt-4o' (only exact id matches, if any), or words that must all appear in the model ids or provider, such as 'anthropic'. Omitted: every model." }, "today": { "type": "string", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "description": "Date to count days from, YYYY-MM-DD. Omitted: today in UTC." }, "includePast": { "type": "boolean", "default": false, "description": "Include shutdowns whose date has passed" } } }arguments 21 linesab_test_sample_size unknown never probed
Returns how many results each version of a prompt or model needs before a difference in how often results work first time is statistically real (two-proportion z-test, two-sided), the total across both versions, and how many days that takes at a given daily volume.
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "required": [ "baselinePct", "targetPct" ], "properties": { "powerPct": { "enum": [ 80, 90 ], "type": "number", "default": 80, "description": "Chance of detecting a real difference of this size, percent" }, "targetPct": { "type": "number", "description": "Share of version B's results that work first time, percent (above 0, below 100)", "exclusiveMaximum": 100, "exclusiveMinimum": 0 }, "baselinePct": { "type": "number", "description": "Share of version A's (the current prompt or model) results that work first time, percent (above 0, below 100)", "exclusiveMaximum": 100, "exclusiveMinimum": 0 }, "confidencePct": { "enum": [ 90, 95, 99 ], "type": "number", "default": 95, "description": "Confidence level, percent. Two-sided." }, "resultsPerDay": { "type": "number", "description": "Results per day across both versions, split evenly. Gives the number of days.", "exclusiveMinimum": 0 } } }arguments 46 linesjudge_agreement unknown 1h ago
Compares a person's verdicts with an AI judge's verdicts on the same results. Returns the agreement rate, Cohen's kappa with confidence intervals, precision and recall per verdict, the agreement of a judge that always gives the most common verdict, a trust verdict (kappa 0.6 or more with at least 80% recall on every verdict; 0.4 to 0.6 is borderline; under 30 results is too few) and the rows where the two disagree.
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "properties": { "csv": { "type": "string", "maxLength": 500000, "description": "The same data as CSV or TSV text instead of rows: columns named human and judge (and optionally note), or the first two columns in that order." }, "rows": { "type": "array", "items": { "type": "object", "required": [ "human", "judge" ], "properties": { "note": { "type": "string", "maxLength": 300, "description": "What the result was, to recognise it in the disagreements" }, "human": { "type": "string", "maxLength": 100, "minLength": 1, "description": "The person's verdict on one result" }, "judge": { "type": "string", "maxLength": 100, "minLength": 1, "description": "The AI judge's verdict on the same result" } } }, "maxItems": 5000, "description": "One row per result. Common words are mapped onto two verdicts: worked (worked, pass, yes, true, 1, good, kept, accepted, ok) and didn't work (didn't work, fail, no, false, 0, bad, redo, retry, rejected, wrong). Other labels are compared as written." } } }arguments 42 lines
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0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.