MCP Server for Z.AI - A Model Context Protocol server that provides AI capabilities
Nothing to run from here
InferIndex 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.
8
tools declared
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trust score
remote
at its publisher
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calls last 7d
What it does
LLM API prices across 70+ providers: cheapest offer, comparisons, history and cost estimates.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user --transport http inferindex-mcp https://mcp.inferindex.dev/mcp
# 2 — ask your agent for something these tools do
# search_models, cheapest, compare_providers and 5 more
InferIndex costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publishernot claimed
INAll 1 server
InferIndex1 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 inferindex-mcp https://mcp.inferindex.dev/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
8 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.
Find the exact id of an LLM tracked by InferIndex from a name or partial name (e.g. 'deepseek', 'qwen3 max', 'claude opus'). Returns matching model ids and names, best match first.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| query | string | yes | Model name or part of it |
Cheapest current API offers for one model across direct providers and aggregators, in USD per 1M tokens (input, output, blended 3:1). Stale prices, and flex/batch tiers, are excluded by default. Optional filters (context, tools, JSON, vision, region, no training on prompts, open sign-up) and usage (tokens per request, requests per day) to get an estimated cost per request and per month. Returns th
| Parameter | Type | Required | Notes |
|---|---|---|---|
| model | string | yes | Model id or name, e.g. 'deepseek-v3.2', 'deepseek/deepseek-v4-pro', 'gpt-5.6-luna'. Use search_models when unsure. |
| min_context | integer | no | Minimum context window in tokens |
| tools | boolean | no | Only offers that support tool calling |
| json | boolean | no | Only offers that support JSON output |
| vision | boolean | no | Only offers that accept image input |
| region | string | no | Only providers that process data in this region: eu, us, … |
| no_training | boolean | no | Only providers whose published terms say they do not train on your prompts |
| no_waitlist | boolean | no | Only providers with open sign-up (no waitlist, invitation or country restriction) |
| strict | boolean | no | Exclude offers whose provider does not publish the filtered information (by default they are kept and flagged) |
| include_tiers | string | no | Also include lower-priority service tiers, comma-separated: flex, batch (hidden by default) |
| prompt_tokens | integer | no | Input tokens per request, for the estimated cost |
| output_tokens | integer | no | Output tokens per request, for the estimated cost |
| cached_ratio | number | no | Share of input tokens served from the provider's prompt cache (0 to 1) |
| requests_per_day | integer | no | Requests per day, to also get an estimated monthly cost |
| limit | integer | no | Number of offers to return, winner included (default 5, max 25) |
| detail | string | no | compact (default): the winner in detail and one short line per other offer (provider, prices, signals, training on prompts when published). full: every field of every offer (all published conditions, |
Current offers for one model, one line per provider and source (direct or via an aggregator), cheapest first (10 by default), with price, quantization, signals and training on prompts when published; the cheapest offer comes in detail. Pass detail: "full" for context, every published condition (data regions, sign-up) and reliability from official status pages on each offer.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| model | string | yes | Model id or name, e.g. 'deepseek-v3.2', 'deepseek/deepseek-v4-pro', 'gpt-5.6-luna'. Use search_models when unsure. |
| sort | string | no | Sort order (default blended); estimated_cost needs prompt_tokens or output_tokens |
| limit | integer | no | Number of offers to return, cheapest included (default 10, max 50) |
| detail | string | no | compact (default): the winner in detail and one short line per other offer (provider, prices, signals, training on prompts when published). full: every field of every offer (all published conditions, |
| region | string | no | Only providers that process data in this region: eu, us, … |
| no_training | boolean | no | Only providers whose published terms say they do not train on your prompts |
| no_waitlist | boolean | no | Only providers with open sign-up (no waitlist, invitation or country restriction) |
| strict | boolean | no | Exclude offers whose provider does not publish the filtered information (by default they are kept and flagged) |
| include_tiers | string | no | Also include lower-priority service tiers, comma-separated: flex, batch (hidden by default) |
| prompt_tokens | integer | no | Input tokens per request, for the estimated cost |
| output_tokens | integer | no | Output tokens per request, for the estimated cost |
| cached_ratio | number | no | Share of input tokens served from the provider's prompt cache (0 to 1) |
| requests_per_day | integer | no | Requests per day, to also get an estimated monthly cost |
Price history of one model: every offer tracked by InferIndex (daily or weekly min/max/last price in USD, or raw price changes), plus the official price of the model's lab over time. Give either days, or from/to (YYYY-MM-DD), or at (a date) for the prices in effect that day.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| model | string | yes | Model id or name, e.g. 'deepseek-v3.2', 'deepseek/deepseek-v4-pro', 'gpt-5.6-luna'. Use search_models when unsure. |
| days | integer | no | Number of days back from today (default 7) |
| from | string | no | Start date, YYYY-MM-DD (with to, instead of days) |
| to | string | no | End date, YYYY-MM-DD |
| at | string | no | A single date, YYYY-MM-DD: prices in effect that day |
| granularity | string | no | day (default), week, or raw price changes |
| provider | string | no | Only this provider |
| limit | integer | no | Maximum number of points (default 100, max 500) |
Estimated cost of a workload on one model at each provider: cost per request, and per month if requests_per_day is given, taking the provider's tiered pricing and prompt-cache price into account. Offers sorted by estimated cost, cheapest first: the cheapest in detail, then one short line per offer with its estimated cost.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| model | string | yes | Model id or name, e.g. 'deepseek-v3.2', 'deepseek/deepseek-v4-pro', 'gpt-5.6-luna'. Use search_models when unsure. |
| prompt_tokens | integer | no | Input tokens per request, for the estimated cost |
| output_tokens | integer | no | Output tokens per request, for the estimated cost |
| cached_ratio | number | no | Share of input tokens served from the provider's prompt cache (0 to 1) |
| requests_per_day | integer | no | Requests per day, to also get an estimated monthly cost |
| limit | integer | no | Number of offers to return, cheapest included (default 5, max 25) |
| detail | string | no | compact (default): the winner in detail and one short line per other offer (provider, prices, signals, training on prompts when published). full: every field of every offer (all published conditions, |
GPU types whose rental price InferIndex tracks (H100, A100, L40S…), with the lowest price per GPU per hour in USD for each tier (guaranteed, community, spot) and the number of GPUs of that configuration. Prices are per GPU per hour, as published by each provider, and dated. Tiers are different products and are never compared with each other.
No parameter schema on file.
Rental offers for one GPU type: provider, price in USD per GPU per hour, number of GPUs in the published configuration, billing and region when published. One block per tier (guaranteed, community, spot), each with its own cheapest offer, also given per configuration (cheapest_by_gpu_count: the price per GPU of a single GPU and of a multi-GPU node are not comparable); tiers are never compared with
| Parameter | Type | Required | Notes |
|---|---|---|---|
| gpu | string | yes | GPU id from list_gpus, for example h100-sxm-80gb |
| tier | string | no | Only this tier (default: one block per tier) |
| gpu_count | integer | no | Only this published configuration: 1, 2, 4, 8 or 16 GPUs |
| region | string | no | Only this region, as published by the provider |
| detail | string | no | compact (default): the 3 cheapest offers of each tier. full: up to 50 offers per tier |
Is it cheaper to host an open-weights model yourself on rented GPUs, or to use the cheapest API offer? Returns a one-sentence verdict (headline) and the numbers behind it: the break-even volume in million tokens per day, the throughput the whole configuration must deliver for self-hosting to cost less (required_tokens_per_second: compare it with what your setup does), the break-even utilization wh
| Parameter | Type | Required | Notes |
|---|---|---|---|
| model | string | yes | Model id or name, e.g. 'deepseek-v3.2', 'deepseek/deepseek-v4-pro', 'gpt-5.6-luna'. Use search_models when unsure. |
| tokens_per_day | integer | no | Your volume in tokens per day (input and output together). Or give requests_per_day, prompt_tokens and output_tokens |
| requests_per_day | integer | no | Requests per day (with prompt_tokens and output_tokens) |
| prompt_tokens | integer | no | Input tokens per request |
| output_tokens | integer | no | Output tokens per request |
| utilization | number | no | Average share of time the GPUs serve requests, in percent (default 50) |
| tokens_per_second | number | no | Your own measured throughput for the whole configuration, input and output tokens together, instead of our estimate |
| weights_margin | integer | no | Percent of the GPU memory the model weights may fill (default 70; the rest is the engine reserve and the context cache) |
| gpu | string | no | Impose a GPU: id from list_gpus, for example h100-sxm-80gb |
| gpu_count | integer | no | Impose a number of GPUs: 1, 2, 4, 8 or 16 |
| quantization | string | no | Precision of the weights (default fp8); must be published by at least one provider unless fp16 or bf16 |
| tier | string | no | GPU rental tier (default guaranteed) |
| detail | string | no | compact (default): verdict, headline and key numbers. full: also the three throughput scenarios, the model and every assumption |
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 InferIndex or to call it, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
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Alert at 80% · then the tool returns a normal MCP error
Used this month: —
—Grade not published
No rule-by-rule report on file for this release
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.
This listing is an address its publisher runs, and you connect to it directly — this marketplace is not in the path. The scanner reads packages and public source, and an address is neither: the repository linked on this page is where this record was collected from, not a copy of what the address serves, so it is not graded in its place. So there is no grade — not a withheld one, an unmeasured one.
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 1.0.0.
1.0.0
undated
latestNo release note was published with this version.
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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 InferIndex can review it, once — before installing it or after. A review from an account that has installed it is marked as one.
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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.