AI Wave

Last 30 days

Uptime

100%

Response time

976 ms typical, 1.0 s slowest 5%

Last check

3 h ago

Next check

in 4 h

How to call it

Add it to any MCP client that supports remote servers.

{
  "mcpServers": {
    "ai-wave": {
      "type": "http",
      "url": "https://aiwave.elopstudio.com/api/mcp"
    }
  }
}

8 tools

• list_model_changes

  Changes across AI models: new releases, price changes, API changes and deprecations. Normalized from vendor release notes, Hugging Face, the OpenRouter catalog, GitHub and the LiteLLM price map, deduplicated per model, each marked official or pending review. Poll with `since` (un

• get_model

  Pricing, context length and catalog status for one model. Accepts an OpenRouter id (anthropic/claude-opus-5) or a Hugging Face repo id.

• get_price_history

  Price history for one model: when the price changed and to what. The public catalog only exposes the current price, so this answers 'was this cheaper last month?'. Each entry holds until the next one. History starts when AI Wave began recording, not when the model launched.

• search_models

  Shortlist models by budget, context and capability, ranked by measured performance. Use this to answer 'which model should I use for X': it returns benchmark scores alongside price so the trade-off is visible in one call. Sort by a subject (math, coding, science, reading) to find

• compare_models

  Put two or more models side by side: price, context, benchmark scores per subject, and what each costs per month at a given volume. Use this instead of calling get_model repeatedly: it aligns the fields and marks which subjects a model has not been tested on, so a missing score i

• estimate_cost

  What one model costs per month at a given token volume, in USD and KRW. Token prices are per million and hard to reason about directly; this turns them into a monthly bill. Input and output are mixed 75/25 unless you pass your own split.

• get_today

  Today in one call: the top 5 models, which ones climbed, whose pricing changed in the last 24h, and what was newly listed in the last 7 days. Use this instead of paging list_model_changes and re-deriving the summary: price changes are already collapsed to one per model, with the

• find_replacement

  What to switch to when a model is gone or you need a fallback. Ranked by closeness in measured performance, not by vendor or price: what you usually need to preserve first is the quality of the output. Candidates whose context window is less than half the original are excluded. R

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