MCP Server for Z.AI - A Model Context Protocol server that provides AI capabilities
Nothing to run from here
AI Rate Card is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
96/100
tool-list score
13
tools declared
remote
at its publisher
—
calls last 7d
What it does
Daily-checked AI model API prices from 120+ providers: search, compare, cheapest, cost estimates.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user --transport http ai-rate-card-mcp https://airatecard.com/mcp
# 2 — ask your agent for something these tools do
# search_offers, get_model_prices, cheapest_offers and 10 more
AI Rate Card costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publishernot claimed
AIAll 1 server
airatecard1 server
1 category
Collected from a public index. Nobody has claimed this account, so nothing here was written by its author — claim it if it is yours.
Installation
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. Nothing here runs on your machine and nothing runs on ours: this server answers at its publisher’s own address, and what you install is the connection straight to it.
Claude Codedeclared by this listing
This is a public server: it answers at its publisher’s own address and this marketplace is not in the path. Claude Code registers it in one command; --scope user makes it available in every project. The transport below is http, as the publisher's own manifest declares it.
claude mcp add --scope user --transport http ai-rate-card-mcp https://airatecard.com/mcpReconnect, or start a new session, and the tools appear in the model’s tool list.
Free on mcprush, and it answers at its publisher’s own address: this marketplace is not in the path, so there is no key of ours to hold and no card to add.
What it writes
One config entry pointing straight at the publisher’s own address — this marketplace is not in the path, and nothing from this listing executes on your machine.
What you approve
13 tools, with what each one reads, writes and reaches shown before you agree — the same list on every route above. Read the tool surface.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
Search current AI model offers (one model at one provider = one offer) by words, model type and limits; priced offers first, doubtful prices after sane ones. Each row has offer_id, model_id, provider_id, prices in USD per 1M tokens (or per unit), card_url and a cite line. Next: get_card {"card_id": "offer:<offer_id>"} for one offer, get_model_prices for one model everywhere, estimate_cost / cheape
| Parameter | Type | Required | Notes |
|---|---|---|---|
| query | string | no | words of the model, seller or display name |
| category | string | no | model type: llm (text models), embedding, image_generation, text_to_speech ...; none = every type |
| multi | boolean | no | also (or only, with no category) the multimodal offers that qualify (WP-0086) |
| max_input_price | number | no | USD per 1M input tokens, at most |
| min_context | integer | no | context window in tokens, at least |
| input_modality | string | no | e.g. text, image, audio |
| free_only | boolean | no | only offers that cost nothing (default false) |
| sort | string | no | order, price_input (cheapest input first) by default |
| limit | integer | no | rows, 20 by default |
Every seller's current price of one model (an id, or a name to search for), cheapest input first.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| model | string | yes | model id or a name to search for |
The cheapest current offers (rows: one model at one provider) of one model type, by input price. For the cheapest MODELS of a job (one row per model) use cheapest_models_by_task; for a monthly workload use cheapest_for_workload.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| category | string | no | model type, llm (text models) by default |
| limit | integer | no | rows, 10 by default |
2 to 5 models side by side: the best current offer of each and, for up to 4, every field compared (lowest and median input/output price, context, max output, providers, regions, a 1M-in/200k-out workload cost), who wins by how many percent, providers selling all, and summary sentences.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| models | array | yes | model ids (e.g. openai/gpt-4o-mini, as model_id in search_offers rows) or names to search for |
The cost of a workload (input, output and cached input tokens, number of requests) at every current provider of one model, cheapest first, in USD - what the same job costs where.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| model | string | yes | model id or a name to search for |
| input_tokens | integer | no | input tokens of the whole job, e.g. 1000000 |
| output_tokens | integer | no | output tokens of the whole job, e.g. 200000 |
| cached_input_tokens | integer | no | of the input, tokens read from the prompt cache |
| requests | integer | no | number of calls (for per-request prices) |
One card with its citation. card_id is '<kind>:<key>': offer:<provider>|<model id as the provider writes it> (e.g. offer:anthropic|claude-opus-4), model:<creator>/<model> (e.g. model:anthropic/claude-opus-4), seller:<provider key> (e.g. seller:anthropic). Ids come from search_offers (offer_id, model_id, provider_id) and from the card_url of every result. Works for every seller, a hidden test selle
| Parameter | Type | Required | Notes |
|---|---|---|---|
| card_id | string | yes | offer:<provider>|<model>, model:<creator>/<model> or seller:<key> |
For a provider (seller) joining the catalog: check a models.yaml before putting it online - the same check as the wizard and POST /v1/provider/validate. Give the file's text in models (YAML, or the same structure as JSON text); domain (the provider's site) makes the model_page checks exact. Returns ok, the problems (file, line, model, path, value, code, message, hint - fix each and call again)
| Parameter | Type | Required | Notes |
|---|---|---|---|
| models | string | yes | the models.yaml text |
| domain | string | no | the provider's domain, e.g. example.ai |
| name | string | no | the provider's name |
The cheapest offers for a whole monthly workload, ranked by monthly cost in USD: give a preset (a typical job; see list_workload_presets) and/or your own tokens a month. Best offer of each model; free promos and batch/flex/priority/regional prices left out unless asked. Each row: monthly_cost_usd, prices, context, checked_at, card_url and a ready cite line.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| preset | string | no | chatbot | coding_agent | rag | summarization | long_context | vision | embedding_index | trial - fills tokens, min_context, category; your own values win |
| input_tokens | integer | no | input tokens a month, e.g. 10000000 |
| output_tokens | integer | no | output tokens a month, e.g. 2000000 |
| min_context | integer | no | context window needed, tokens, e.g. 128000 |
| category | string | no | model type, llm by default |
| input_modality | string | no | the model must accept this input, e.g. image |
| tools | boolean | no | the model must support tool/function calling |
| query | string | no | only models or providers matching these words, e.g. claude |
| include_free | boolean | no | also free / promo offers (default false) |
| include_special | boolean | no | also batch, flex, priority, regional prices (default false) |
| one_per_model | boolean | no | only the best offer of each model (default true) |
| limit | integer | no | rows, 10 by default |
The ready-made workloads of cheapest_for_workload: name, what job it is, tokens a month, context needed, model type.
No parameter schema on file.
AI Rate Card's articles on AI model prices (the cheapest models by task, price moves, costs), newest first, with their links - to quote or point a reader to.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| lang | string | no | language of the articles, en by default |
The n cheapest MODELS for a job (task): each model counted once at its own lowest current price (input tokens by default). One row per model with that price, where it is and card_url.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| task | string | yes | what the buyer wants done: text (chat/LLM), vision, image, video, tts (speech from text), stt (text from speech), embedding, rerank, moderation - a job, not a model type (search_offers' category is th |
| component | string | no | input_tokens (default), output_tokens, or the task's own unit |
| scope | string | no | all (default), specialized (the task's own category) or multi/universal (qualifying llm models - the two names mean the same set, WP-0086) |
| n | integer | no | 10 by default |
The n most expensive MODELS for a job (task): each model counted once at its own lowest current price, highest first - the top of the market. Same arguments as cheapest_models_by_task.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| task | string | yes | what the buyer wants done: text (chat/LLM), vision, image, video, tts (speech from text), stt (text from speech), embedding, rerank, moderation - a job, not a model type (search_offers' category is th |
| component | string | no | input_tokens (default), output_tokens, or the task's own unit |
| scope | string | no | all (default), specialized (the task's own category) or multi/universal (qualifying llm models - the two names mean the same set, WP-0086) |
| n | integer | no | 10 by default |
The typical (median) current price of a job (task) across models, and the n models closest to it - what a normal price for this job is. Same arguments as cheapest_models_by_task.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| task | string | yes | what the buyer wants done: text (chat/LLM), vision, image, video, tts (speech from text), stt (text from speech), embedding, rerank, moderation - a job, not a model type (search_offers' category is th |
| component | string | no | input_tokens (default), output_tokens, or the task's own unit |
| scope | string | no | all (default), specialized (the task's own category) or multi/universal (qualifying llm models - the two names mean the same set, WP-0086) |
| n | integer | no | 10 by default |
Free
$0 forever
- Every tool — any call limit is its publisher’s, not ours
- No card, and mcprush charges nothing for it
- Runs at its publisher’s own address — this marketplace is not in the path
- Nothing to cap, because mcprush bills nothing
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable: mcprush charges nothing to install AI Rate Card or to call it, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
—
—
Alert at 80% · then the tool returns a normal MCP error
Used this month: —
ATool-list grade · score 96 / 100
Release 1.0.1 · scanned 5 Nov 2026
Graded from the tools this server lists, not from its source code.
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
Threat score100
Client score96
Findings0 threat · 0 capability
Capabilityminimal
Coveragemanifest
Verificationnot in this report
Surface digest071ae9690c1a95f9203be6dbc4dd9f505ee61f8bb7fad3d0982afa3c1a75c1f3
Score vectorthreat 100 → client 96
| Term | Level | What it prices | Points |
|---|---|---|---|
| capability-exposure | minimal | capability blast radius (minimal) — client exposure if the model is manipulated | −0 |
| coverage-honesty | manifest | inspection depth (manifest) — how much of the target the scan could see | −4 |
score = clamp(round(threatScore − E_cap − E_ver − E_cov), 0, 100). No model, no opinion, no network call at scoring time: the same methodology version against the same bytes always produces this number, and every line above is a field of the report rather than a summary of it.
What the scan could actually read
A grade is only as meaningful as its coverage, so the scanner publishes its own depth before it publishes its result.
The scan read each tool’s name, description, input schema and annotations exactly as the server listed them the last time it answered us; no code was reviewed. We ask the server again about once a month, and a list that changed is graded again.
Tool surfaceread
Implementation sourcenot available
Package metadatanot available
Live transportnot available
The implementation source was not read — this reflects the declared tool metadata only.
Capability — what it could do if the model were manipulated
Tags derived from each tool’s schema and the implementation, not from what the tool calls itself. minimal is the level these add up to.
| Tool | Capability tags | Why the tag was assigned |
|---|---|---|
| search_offers | no tags | |
| get_model_prices | no tags | |
| cheapest_offers | no tags | |
| compare_models | no tags | |
| estimate_cost | no tags | |
| get_card | no tags | |
| validate_provider_files | no tags | |
| cheapest_for_workload | no tags | |
| list_workload_presets | no tags | |
| list_articles | no tags | |
| cheapest_models_by_task | no tags | |
| most_expensive_models_by_task | no tags | |
| median_price_by_task | no tags |
Toxic-flow graph
The lethal trifecta, checked as a graph rather than as a checklist: untrusted input, a sensitive source and an external sink have to meet before there is a path worth worrying about.
The public result for this release does not print the flow graph, so there is nothing to show here. That is not the same as "no paths were found": what the scan did read is above, under coverage.
Supply chain and provenance
Integrityfirst-seen
Surface digest071ae9690c1a95f9
Scanned2026-11-05
This is the first scan of this surface here, so there is nothing yet to compare it against.
Every result on this tab comes from one deterministic pass over the published package — offline, rule by rule, and auditable line by line above. Same methodology version, same bytes, same score.
Release history
This is an address its publisher runs: which release it serves is theirs to choose, so there is nothing here to pin. The history below is for reading; the current release on file is 1.0.1.
1.0.1
undated
latestNo release note was published with this version.
—
no reviews yet
Who can review
One review per account, from a signed-in account that does not publish this listing. Nothing else is asked — you can say what you think before you install it. Publishers can reply once per review, and a review can be edited or deleted by whoever wrote it.
Sign in to write one
Any signed-in account that does not publish AI Rate Card can review it, once — before installing it or after. A review from an account that has installed it is marked as one.
No reviews yet
Nobody has reviewed this listing. The rating on the card is the mean of the reviews written here and nothing else, so there is no rating until somebody writes the first — which takes a signed-in account that does not publish it, and nothing else.