MCP server

AI Wave

com.elopstudio.aiwave/ai-wave

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

AI model releases, price changes and deprecations in one feed: chat, embedding, speech, video.

Website Source

At a glance

Type

MCP server

Publisher

elopstudio.com (verified by the MCP Registry)

Status

Working at last check

Tools

8

Token cost

About 1,514 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http ai-wave https://aiwave.elopstudio.com/api/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.ai-wave]
url = "https://aiwave.elopstudio.com/api/mcp"

Gemini CLI

{"mcpServers":{"ai-wave":{"httpUrl":"https://aiwave.elopstudio.com/api/mcp"}}}

Cursor

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

VS Code

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

Antigravity

agy mcp add ai-wave https://aiwave.elopstudio.com/api/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"ai-wave","server_url":"https://aiwave.elopstudio.com/api/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"ai-wave","server_url":"https://aiwave.elopstudio.com/api/mcp"}

Tools

• compare_models ~191 tokens
  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 is not read as a low
• estimate_cost ~168 tokens
  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.
• find_replacement ~158 tokens
  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. Returns the score, pr
• get_model ~75 tokens
  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 ~132 tokens
  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.
• get_today ~109 tokens
  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 raw count kept.
• list_model_changes ~221 tokens
  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` (unix seconds) and feed
• search_models ~460 tokens
  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 a model good at one

Browse

• Claude tools
• GitHub tools
• GitHub and code hosting
• Searching your documents (RAG)

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