io.github.CloudCorpRecords由 GitHub 账号 CloudCorpRecords 发布v0.1.0更新于 Nov 7, 2026
AI model releases, price changes, comparisons and cost estimates in one normalised feed.
本地桥接服务，把托管端的 AI 模型发布、价格变动、对比与成本估算信息流以 MCP 工具形式提供给助手。
Desktop only，通过 STDIO。 STDIO 服务会启动本地进程，因此需要 SourceWeft 桌面宿主。
参照 仓库 中的启动说明。
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.
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.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.)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).
Each tool is also a subcommand taking one JSON argument string (omit it for tools that take none):
Exit codes: 0 on success, 1 on an isError
result, 2 on a usage error.
MCP client config:
The container's default command is mcp, so the first
form is a stdio MCP server; any other arguments reach
the CLI normally.
The test suite runs entirely against a stubbed JSON-RPC upstream — no live network calls.
MIT — see LICENSE.
来源：README.md，提交 a3ae0f0
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.
typelimitmodelsincePricing, context length and catalog status for one model. Accepts an OpenRouter id (anthropic/claude-opus-5) or a Hugging Face repo id.
id必填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.
id必填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.
modelimitsortByvendoracceptsoutputsminContextmaxInputPriceminIntelligencePut 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.
ids必填monthlyMillionTokensWhat 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.
id必填inputSharemonthlyMillionTokensToday 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.
无需输入参数。
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.
id必填已满足适用检查项的 90%
该评分衡量此收录条目的完整程度，依据注册中心条目、端点与公开来源计算得出。它不是对服务器本身的评价，也不表示服务器安全或当前可用：可连通性是带日期的检查结果，评分也不包含安装量或用户评价。
请在注册中心条目中声明凭据请求头，或保持端点能够响应探测，以便判定其认证方式。
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 --versionnpx -y @reneturcios/modelpulse mcp{ "mcpServers": { "modelpulse": { "command": "npx", "args": ["-y", "@reneturcios/modelpulse", "mcp"] } }}docker run -i ghcr.io/cloudcorprecords/modelpulsedocker run --rm ghcr.io/cloudcorprecords/modelpulse --versionnpm installnpm run buildnpm testnpm run dev