Modelpulse

io.github.CloudCorpRecords由 GitHub 帳號 CloudCorpRecords 發布v0.1.0更新於 Nov 7, 2026

AI model releases, price changes, comparisons and cost estimates in one normalised feed.

已驗證STDIO僅桌面資料與分析AI 與機器學習

概覽

AI 產生的概覽

本機橋接服務,把託管端的 AI 模型發布、價格變動、比較與成本估算資訊流以 MCP 工具形式提供給助理。

可以做什麼
  • 查詢並篩選新模型發布、價格變動、API 變更與下架的變更資訊流
  • 查看單一模型的定價、上下文長度、目錄狀態及歷史價格
  • 依預算、上下文、能力與基準分數搜尋和篩選模型
  • 並排比較二到六個模型,並估算每月美元與韓元成本
  • 取得當日熱門模型、上升榜與近期變動摘要,並為已下架模型尋找替代方案
適用情境
適合在挑選 AI 模型、追蹤廠商發布與價格變化、估算 token 花費,或在模型下架後尋找替代品時使用。它是唯讀的資訊來源,不能用來實際執行模型。
執行需求
透過 npx(需 Node.js)或已發布的 Docker 映像在本機執行;預設不需要任何憑證。需要能連線到上游託管的 MCP 端點,可用 AIWAVE_MCP_URL 覆寫。設定選用的 AIWAVE_API_KEY 時會以 bearer 權杖送出。
安裝前請注意
每次工具呼叫都會轉送到第三方託管端點,查詢內容會離開本機。選用的 AIWAVE_API_KEY 會以 Authorization bearer 標頭送往上游。成本數字來自上游估算,價格歷史僅從上游開始記錄時起算。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Modelpulse,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

modelpulse

modelpulse is a thin local bridge to AI Wave's hosted MCP server, a single normalised feed over the AI model landscape: vendor release notes, the Hugging Face and OpenRouter catalogues, GitHub and the LiteLLM price map, deduplicated per model. It tracks what changed, what things cost, and what to switch to when a model goes away.

The bridge keeps no catalogue and computes nothing. It serves the upstream server's tool list locally — exactly as captured from the live endpoint — and forwards each tool call upstream, returning the result untouched.

Tools

  • list_model_changes — the change feed: new releases, price changes, API changes and deprecations, each entry marked official or pending review. Optional: since (unix seconds; poll by feeding the returned latest back in), type (new_model, price_change, api_change, open_source_surge, deprecation), model (vendor slug, e.g. claude), and limit (1-100, default 20).
  • get_model — pricing, context length and catalogue status for one model (id, required: an OpenRouter id such as anthropic/claude-opus-5, or a Hugging Face repo id).
  • get_price_history — when one model's price changed and to what (id, required). History starts when AI Wave began recording, not when the model launched.
  • search_models — shortlist models by budget, context and capability, with benchmark scores next to prices. Optional: vendor, maxInputPrice (USD per 1M input tokens), minContext, minIntelligence, sortBy (a subject such as coding or math, or buzz for popularity), outputs, mode, accepts, and limit (1-50, default 10).
  • compare_models — two to six models side by side (ids, required): price, context, per-subject scores (untested subjects are marked, not zeroed), and the monthly cost at a volume (monthlyMillionTokens, default 10).
  • estimate_cost — one model's monthly bill in USD and KRW (id, required), from monthlyMillionTokens (default 10) and an inputShare split (0-1, default 0.75 input / 0.25 output).
  • get_today — today in one call: the top 5 models, climbers, pricing changes from the last 24h, and new listings from the last 7 days. No arguments.
  • find_replacement — what to switch to when a model is gone (id, required), ranked by closeness in measured performance, with score, price and context deltas. Candidates with less than half the original's context window are excluded.

Configuration

  • AIWAVE_MCP_URL (optional) — override the upstream endpoint. Defaults to https://aiwave.elopstudio.com/api/mcp. (The override hook is this rebuild's own labelled reconstruction.)
  • AIWAVE_API_KEY (optional) — when set, every upstream request carries Authorization: Bearer <key>. When it is unset, no auth header is sent; the capture this rebuild is based on was taken keyless. (This forwarding is this rebuild's own labelled reconstruction.)

Forwarding mechanics

On the first tool call the client opens a streamable HTTP session with the upstream: a JSON-RPC initialize request (protocolVersion 2025-03-26, clientInfo name modelpulse), then a notifications/initialized notification, then the tools/call request carrying the caller's arguments unchanged. When the upstream answers initialize with an Mcp-Session-Id header, that value rides on every later request of the session, which is reused for the life of the process. Replies may arrive as plain JSON or as server-sent events; both are read.

Successful upstream results pass through verbatim, including any isError flag the upstream sets. Failures — a JSON-RPC error, a non-2xx status, an unreadable response, or a request that cannot be completed — come back as an isError result whose text starts with modelpulse error: (this rendering is this rebuild's own labelled reconstruction).

CLI

Each tool is also a subcommand taking one JSON argument string (omit it for tools that take none):

bash
npx @reneturcios/modelpulse get_todaynpx @reneturcios/modelpulse get_model '{"id":"anthropic/claude-opus-5"}'npx @reneturcios/modelpulse compare_models '{"ids":["anthropic/claude-opus-5","openai/gpt-5.2"]}'npx @reneturcios/modelpulse --version

Exit codes: 0 on success, 1 on an isError result, 2 on a usage error.

MCP server

bash
npx -y @reneturcios/modelpulse mcp

MCP client config:

json
{  "mcpServers": {    "modelpulse": {      "command": "npx",      "args": ["-y", "@reneturcios/modelpulse", "mcp"]    }  }}

Docker

bash
docker run -i ghcr.io/cloudcorprecords/modelpulsedocker run --rm ghcr.io/cloudcorprecords/modelpulse --version

The container's default command is mcp, so the first form is a stdio MCP server; any other arguments reach the CLI normally.

Development

bash
npm installnpm run buildnpm testnpm run dev

The test suite runs entirely against a stubbed JSON-RPC upstream — no live network calls.

License

MIT — see LICENSE.

來源:README.md,提交 a3ae0f0

工具

8

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` (unix seconds) and feed the returned `latest` back next time.

輸入參數typelimitmodelsince

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.

輸入參數id必填

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.

輸入參數id必填

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 a model good at one thing.

輸入參數modelimitsortByvendoracceptsoutputsminContextmaxInputPriceminIntelligence

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 is not read as a low one.

輸入參數ids必填monthlyMillionTokens

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.

輸入參數id必填inputSharemonthlyMillionTokens

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 raw count kept.

無需輸入參數。

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. Returns the score, price and context deltas so you can judge; we do not pick for you.

輸入參數id必填

版本歷史

1
  1. v0.1.0最新Nov 4, 2026

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