ai-compute-radar

endpoint

https://aicomputeradar.dev/api/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

karma

0 · newcomer

Is ai-compute-radar live?

Yes — it answered the hub's last check (checked 3h ago). It answered 100% of checks over the last 30 days.

Is ai-compute-radar free to use?

Yes — the hub reached it with no key and no payment.

What tools does ai-compute-radar have?

6 tools: trending_models, weekly_pick, find_fit, gpu_prices, price_history, list_hardware.

Is ai-compute-radar 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.

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_hardware reads open 3h ago

  Curated GPU and Mac profiles the fit engine knows — ids, memory, usable memory after margins, bandwidth. Use an id with find_fit.

  mcp-tool

  12345

  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "properties": {}
  }

  arguments 5 lines copy
• trending_models reads unknown never probed

  Tracked AI models ranked by Heat Score (0–100, weighted percentiles of measured Hugging Face/OpenRouter signals) with the raw signals, local-run facts (GGUF size, quantization) and links. Models still collecting a week of history have heat=null and rank after scored ones.

  mcp-tool

  12345678910111213141516

  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "properties": {
      "slug": {
        "type": "string",
        "description": "Return a single model by slug."
      },
      "limit": {
        "type": "integer",
        "maximum": 100,
        "minimum": 1,
        "description": "How many models to return (default 12)."
      }
    }
  }

  arguments 16 lines copy
• weekly_pick reads unknown never probed

  The current pick of the week: one tracked model chosen by a published rule (largest counted Heat Score rise among models that run comfortably on a consumer card of up to 24 GB), with the numbers frozen at selection time, a device-by-device fit ladder and the written report including its caveats. Pass week (e.g. 2026-w37) for a past issue. issue is null until the first issue is published.

  mcp-tool

  1234567891011

  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "properties": {
      "week": {
        "type": "string",
        "pattern": "^\\d{4}-w\\d{2}$",
        "description": "ISO week label of a past issue, e.g. 2026-w37 (default: the current issue)."
      }
    }
  }

  arguments 11 lines copy
• find_fit reads unknown 3h ago

  Which tracked models run on a given GPU or Mac: measured GGUF weights + computed context cache + runtime overhead versus usable memory. Returns the best recommendation and every verdict (EXCELLENT/GOOD/TIGHT/OFFLOAD_REQUIRED/NOT_RECOMMENDED/UNKNOWN) with plain-language reasons. Get hardware ids from list_hardware.

  mcp-tool

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  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "required": [
      "hardware"
    ],
    "properties": {
      "kv": {
        "enum": [
          "f16",
          "q8_0",
          "q4_0"
        ],
        "type": "string",
        "description": "KV-cache quantization (default f16)."
      },
      "model": {
        "type": "string",
        "description": "Restrict to one model slug."
      },
      "context": {
        "type": "integer",
        "maximum": 1048576,
        "minimum": 512,
        "description": "Context length in tokens (default 8192)."
      },
      "hardware": {
        "type": "string",
        "description": "Hardware id or page slug, e.g. rtx-4090, mac-studio-m3-ultra-96gb."
      }
    }
  }

  arguments 32 lines copy
• gpu_prices reads unknown never probed

  Median verified on-demand rental price per GPU class on Vast.ai (USD per hour), with min/p75 and offer counts, the collection timestamp, and per class the Rent Index: this week's median against last week and against the first week collected, a trend word, and the days excluded as marketplace glitches, plus RunPod's lowest posted on-demand price per class (a list price, not a median), Clore.ai's median for the same class (a second marketplace, never blended), and the AWS, Azure and Oracle Cloud pay-as-you-go list prices per GPU-hour, each with the instance type, VM size or bare-metal shape the price sits in (list prices, read four times a day, no statement about capacity). The index describes what prices did; it never forecasts.

  mcp-tool

  12345

  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "properties": {}
  }

  arguments 5 lines copy
• price_history unknown never probed

  Daily price history per GPU class and source, up to 90 days: the rows the Rent Index and the 30-day range are computed from. Marketplace days (Vast.ai, Clore.ai) are medians over the day's counted hourly medians with min, p75 and offer count; list-price days (RunPod, AWS, Azure, Oracle Cloud, Lambda, Nebius, CoreWeave, Hyperstack) carry the posted price. Each row says which kind it is; never blend the two. Describes what prices did; it never forecasts.

  mcp-tool

  1234567891011121314151617181920

  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "properties": {
      "gpu": {
        "type": "string",
        "description": "One class by slug or name, e.g. rtx-4090, h100. Every class without it."
      },
      "days": {
        "type": "integer",
        "maximum": 365,
        "minimum": 1,
        "description": "Days back from today (default 30, max 365; the series starts 26 August 2026)."
      },
      "source": {
        "type": "string",
        "description": "One source key, e.g. vast, runpod, aws. Every published source without it."
      }
    }
  }

  arguments 20 lines copy

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

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.