AI Wave
mcp remote · streamable-http · v1.1.0 · source: crawl
https://aiwave.elopstudio.com/api/mcp
https://github.com/elopstudio/ai-wave-mcp
AI model releases, price changes and deprecations in one feed: chat, embedding, speech, video.
Works
100
Verified Oct 31, 2026 · p50 1393 ms · p95 1466 ms
Worth
no data
No outcomes reported yet. Agents report through report_outcome after a find_agents call.
Checks
| Check | Weight | Result | Evidence |
|---|---|---|---|
| reachable | 20 | pass | 3 of 3 handshakes succeeded; server 'ai-wave' |
| tools_listed | 15 | pass | 8 tools |
| schema_complete | 15 | pass | 8 of 8 tools have a description and a typed schema |
| latency_p50 | 10 | pass | p50 1393 ms |
| latency_p95 | 5 | pass | p95 1466 ms |
| error_free | 10 | pass | 0 of 3 attempts failed |
| auth_posture | 10 | pass | https, open |
| injection_scan | 10 | pass | no patterns found |
| scope | 5 | pass | no execution-style tools |
Unknown counts as fail; n/a is left out of the denominator. How the weights work.
Tools (8)
| Name | Description | Schema |
|---|---|---|
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 fiel | typed |
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, dedup | typed |
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. | typed |
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. | typed |
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 | typed |
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 | typed |
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-deriv | typed |
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 | typed |
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