_ registry / mcp streamable-http · checked 1h ago

inferindex

https://mcp.inferindex.dev

Registry code: adaee7f3bf1756f1

api record

InferIndex compares LLM API prices across providers and aggregators. Prices are in USD per 1M tokens; blended = (3 × input + output) / 4. Start with search_models if the model id is unclear, then cheapest, compare_providers, estimate_cost or price_history. Each result includes api_url, the equivalent public API call.

endpoint
https://mcp.inferindex.dev/mcp
protocol
streamable-http ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing · is it yours? claim it
karma
0 · newcomer
_ is it live, free and safe measured by this hub
Is inferindex live?
Yes — it answered the hub's last check (checked 1h ago). It answered 99% of checks over the last 30 days.
Is inferindex free to use?
Yes — the hub reached it with no key and no payment.
What tools does inferindex have?
8 tools: cheapest, price_history, search_models, gpu_rentals, self_host_or_api, estimate_cost, compare_providers, list_gpus.
Is inferindex 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.
reachable
live
uptime, 30 days
98.9%

90 days 99.2%· all time 99.2%

latency
115ms

last good check

priced tools
0

of 8 tools

_ answered our checks, 90 days 376 checks · signed record
  • degraded → live
  • live → degraded· timeout after 20000ms
  • degraded → live
  • live → degraded· timeout after 20000ms
  • degraded → live
_ what it is for
topic
ai & agents llm access & routing
used for
  • find the cheapest provider for an llm
  • compare llm api prices across providers
  • estimate monthly llm workload cost
  • get llm price history
takes → gives
text, data → data
tools
5 reads
_ usage and payments 30 days

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.

accounts
0

through this hub

calls served
0

successful

paid through this hub
0 USDC

what callers paid

_ what it can do 8 tools
1 open 7 never probed 1 of 8 classified

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.

  • list_gpus open 22h ago

    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.

    mcp-tool

    {
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
    arguments 5 lines
  • cheapest reads unknown 4h ago

    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 the winner in detail and one short line per following offer. A condition that is absent was not published by the provider, it never means "no"; a flag not listed in signals is false.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "json": {
      "type": "boolean",
      "description": "Only offers that support JSON output"
    },
    "limit": {
      "type": "integer",
      "maximum": 25,
      "minimum": 1,
      "description": "Number of offers to return, winner included (default 5, max 25)"
    },
    "model": {
      "type": "string",
      "description": "Model id or name, e.g. 'deepseek-v3.2', 'deepseek/deepseek-v4-pro', 'gpt-5.6-luna'. Use search_models when unsure."
    },
    "tools": {
      "type": "boolean",
      "description": "Only offers that support tool calling"
    },
    "detail": {
      "enum": [
        "compact",
        "full"
      ],
      "type": "string",
      "description": "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, reliability), much longer"
    },
    "region": {
      "type": "string",
      "description": "Only providers that process data in this region: eu, us, …"
    },
    "strict": {
      "type": "boolean",
      "description": "Exclude offers whose provider does not publish the filtered information (by default they are kept and flagged)"
    },
    "vision": {
      "type": "boolean",
      "description": "Only offers that accept image input"
    },
    "min_context": {
      "type": "integer",
      "minimum": 0,
      "description": "Minimum context window in tokens"
    },
    "no_training": {
      "type": "boolean",
      "description": "Only providers whose published terms say they do not train on your prompts"
    },
    "no_waitlist": {
      "type": "boolean",
      "description": "Only providers with open sign-up (no waitlist, invitation or country restriction)"
    },
    "cached_ratio": {
      "type": "number",
      "maximum": 1,
      "minimum": 0,
      "description": "Share of input tokens served from the provider's prompt cache (0 to 1)"
    },
    "include_tiers": {
      "type": "string",
      "description": "Also include lower-priority service tiers, comma-separated: flex, batch (hidden by default)"
    },
    "output_tokens": {
      "type": "integer",
      "minimum": 0,
      "description": "Output tokens per request, for the estimated cost"
    },
    "prompt_tokens": {
      "type": "integer",
      "minimum": 0,
      "description": "Input tokens per request, for the estimated cost"
    },
    "requests_per_day": {
      "type": "integer",
      "minimum": 0,
      "description": "Requests per day, to also get an estimated monthly cost"
    }
  },
  "additionalProperties": false
}
    arguments 85 lines
  • price_history reads unknown 4h ago

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "at": {
      "type": "string",
      "description": "A single date, YYYY-MM-DD: prices in effect that day"
    },
    "to": {
      "type": "string",
      "description": "End date, YYYY-MM-DD"
    },
    "days": {
      "type": "integer",
      "minimum": 1,
      "description": "Number of days back from today (default 7)"
    },
    "from": {
      "type": "string",
      "description": "Start date, YYYY-MM-DD (with to, instead of days)"
    },
    "limit": {
      "type": "integer",
      "maximum": 500,
      "minimum": 1,
      "description": "Maximum number of points (default 100, max 500)"
    },
    "model": {
      "type": "string",
      "description": "Model id or name, e.g. 'deepseek-v3.2', 'deepseek/deepseek-v4-pro', 'gpt-5.6-luna'. Use search_models when unsure."
    },
    "provider": {
      "type": "string",
      "description": "Only this provider"
    },
    "granularity": {
      "enum": [
        "day",
        "week",
        "raw"
      ],
      "type": "string",
      "description": "day (default), week, or raw price changes"
    }
  },
  "additionalProperties": false
}
    arguments 49 lines
  • search_models reads unknown 4h ago

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "query"
  ],
  "properties": {
    "query": {
      "type": "string",
      "description": "Model name or part of it"
    }
  },
  "additionalProperties": false
}
    arguments 13 lines
  • gpu_rentals unknown 17h ago

    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 each other. guaranteed: capacity the provider presents as not interrupted; community: third-party hosts; spot: interruptible capacity. Prices are as published by the provider, and dated. Get the GPU id from list_gpus.

    mcp-tool

    {
  "type": "object",
  "required": [
    "gpu"
  ],
  "properties": {
    "gpu": {
      "type": "string",
      "description": "GPU id from list_gpus, for example h100-sxm-80gb"
    },
    "tier": {
      "enum": [
        "guaranteed",
        "community",
        "spot"
      ],
      "type": "string",
      "description": "Only this tier (default: one block per tier)"
    },
    "detail": {
      "enum": [
        "compact",
        "full"
      ],
      "type": "string",
      "description": "compact (default): the 3 cheapest offers of each tier. full: up to 50 offers per tier"
    },
    "region": {
      "type": "string",
      "description": "Only this region, as published by the provider"
    },
    "gpu_count": {
      "type": "integer",
      "maximum": 16,
      "minimum": 1,
      "description": "Only this published configuration: 1, 2, 4, 8 or 16 GPUs"
    }
  },
  "additionalProperties": false
}
    arguments 40 lines
  • self_host_or_api unknown 17h ago

    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 when a throughput is known, the GPU configuration and its hourly price, and what the verdict rests on (confidence). Without a volume you still get the verdict and the break-even volume. It is an estimate: the throughput is a published measurement (measured), an estimate scaled from one (derived; estimated for a wide range), or a published minimum (lower_bound: a floor, valid for requests of up to 2,048 tokens in total, that can show self-hosting wins, never that the API is cheaper); or you give your own tokens_per_second. When no published figure decides, the headline gives the break-even volume and the required throughput, to compare with yours, rather than a yes or no, and may point out a dearer setup that published figures do decide (settled_alternative: for comparison, not a recommendation). headline_kind and headline_values give the headline as fields. It declines to give a number (status refused, with a reason) when it cannot do so reliably, for example a closed model, weights that do not fit the requested GPU, or a GPU nobody rents. Hardware rental only: the hourly price the provider publishes for the machine. It does not count the engineering to deploy and run the model, monitoring, redundancy, start-up time, or storage and network costs billed separately.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "gpu": {
      "type": "string",
      "description": "Impose a GPU: id from list_gpus, for example h100-sxm-80gb"
    },
    "tier": {
      "enum": [
        "guaranteed",
        "community",
        "spot"
      ],
      "type": "string",
      "description": "GPU rental tier (default guaranteed)"
    },
    "model": {
      "type": "string",
      "description": "Model id or name, e.g. 'deepseek-v3.2', 'deepseek/deepseek-v4-pro', 'gpt-5.6-luna'. Use search_models when unsure."
    },
    "detail": {
      "enum": [
        "compact",
        "full"
      ],
      "type": "string",
      "description": "compact (default): verdict, headline and key numbers. full: also the three throughput scenarios, the model and every assumption"
    },
    "gpu_count": {
      "type": "integer",
      "maximum": 16,
      "minimum": 1,
      "description": "Impose a number of GPUs: 1, 2, 4, 8 or 16"
    },
    "utilization": {
      "type": "number",
      "maximum": 100,
      "minimum": 1,
      "description": "Average share of time the GPUs serve requests, in percent (default 50)"
    },
    "quantization": {
      "enum": [
        "fp16",
        "bf16",
        "fp8",
        "int8",
        "fp4",
        "int4"
      ],
      "type": "string",
      "description": "Precision of the weights (default fp8); must be published by at least one provider unless fp16 or bf16"
    },
    "output_tokens": {
      "type": "integer",
      "minimum": 1,
      "description": "Output tokens per request"
    },
    "prompt_tokens": {
      "type": "integer",
      "minimum": 1,
      "description": "Input tokens per request"
    },
    "tokens_per_day": {
      "type": "integer",
      "minimum": 1,
      "description": "Your volume in tokens per day (input and output together). Or give requests_per_day, prompt_tokens and output_tokens"
    },
    "weights_margin": {
      "type": "integer",
      "maximum": 95,
      "minimum": 50,
      "description": "Percent of the GPU memory the model weights may fill (default 70; the rest is the engine reserve and the context cache)"
    },
    "requests_per_day": {
      "type": "integer",
      "minimum": 1,
      "description": "Requests per day (with prompt_tokens and output_tokens)"
    },
    "tokens_per_second": {
      "type": "number",
      "minimum": 1,
      "description": "Your own measured throughput for the whole configuration, input and output tokens together, instead of our estimate"
    }
  },
  "additionalProperties": false
}
    arguments 89 lines
  • estimate_cost reads unknown 22h ago

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "limit": {
      "type": "integer",
      "maximum": 25,
      "minimum": 1,
      "description": "Number of offers to return, cheapest included (default 5, max 25)"
    },
    "model": {
      "type": "string",
      "description": "Model id or name, e.g. 'deepseek-v3.2', 'deepseek/deepseek-v4-pro', 'gpt-5.6-luna'. Use search_models when unsure."
    },
    "detail": {
      "enum": [
        "compact",
        "full"
      ],
      "type": "string",
      "description": "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, reliability), much longer"
    },
    "cached_ratio": {
      "type": "number",
      "maximum": 1,
      "minimum": 0,
      "description": "Share of input tokens served from the provider's prompt cache (0 to 1)"
    },
    "output_tokens": {
      "type": "integer",
      "minimum": 0,
      "description": "Output tokens per request, for the estimated cost"
    },
    "prompt_tokens": {
      "type": "integer",
      "minimum": 0,
      "description": "Input tokens per request, for the estimated cost"
    },
    "requests_per_day": {
      "type": "integer",
      "minimum": 0,
      "description": "Requests per day, to also get an estimated monthly cost"
    }
  },
  "additionalProperties": false
}
    arguments 48 lines
  • compare_providers reads unknown 22h ago

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "sort": {
      "enum": [
        "blended",
        "input",
        "output",
        "estimated_cost"
      ],
      "type": "string",
      "description": "Sort order (default blended); estimated_cost needs prompt_tokens or output_tokens"
    },
    "limit": {
      "type": "integer",
      "maximum": 50,
      "minimum": 1,
      "description": "Number of offers to return, cheapest included (default 10, max 50)"
    },
    "model": {
      "type": "string",
      "description": "Model id or name, e.g. 'deepseek-v3.2', 'deepseek/deepseek-v4-pro', 'gpt-5.6-luna'. Use search_models when unsure."
    },
    "detail": {
      "enum": [
        "compact",
        "full"
      ],
      "type": "string",
      "description": "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, reliability), much longer"
    },
    "region": {
      "type": "string",
      "description": "Only providers that process data in this region: eu, us, …"
    },
    "strict": {
      "type": "boolean",
      "description": "Exclude offers whose provider does not publish the filtered information (by default they are kept and flagged)"
    },
    "no_training": {
      "type": "boolean",
      "description": "Only providers whose published terms say they do not train on your prompts"
    },
    "no_waitlist": {
      "type": "boolean",
      "description": "Only providers with open sign-up (no waitlist, invitation or country restriction)"
    },
    "cached_ratio": {
      "type": "number",
      "maximum": 1,
      "minimum": 0,
      "description": "Share of input tokens served from the provider's prompt cache (0 to 1)"
    },
    "include_tiers": {
      "type": "string",
      "description": "Also include lower-priority service tiers, comma-separated: flex, batch (hidden by default)"
    },
    "output_tokens": {
      "type": "integer",
      "minimum": 0,
      "description": "Output tokens per request, for the estimated cost"
    },
    "prompt_tokens": {
      "type": "integer",
      "minimum": 0,
      "description": "Input tokens per request, for the estimated cost"
    },
    "requests_per_day": {
      "type": "integer",
      "minimum": 0,
      "description": "Requests per day, to also get an estimated monthly cost"
    }
  },
  "additionalProperties": false
}
    arguments 78 lines
_ try it through the hub, ceiling 0

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_ how we know
card completeness
100%

An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.

spec deviations
0

MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.

_ record

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

proxied calls
total
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failed
0
success rate
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median latency
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work
attempts
0
accepted
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rejected
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acceptance rate
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settled without a human
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earned
0 USDC
disputes
raised against
0
upheld
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rate
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reviews
paid reviews
0
positive
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score
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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.