About modelpulse
modelpulse is a local bridge to AI Wave’s hosted MCP server for developers and researchers tracking AI models. It forwards tools for release and price changes, model comparisons, cost estimates, and replacement suggestions without maintaining its own catalogue.
Key features
- 01Track model releases and changes
- 02Inspect model pricing and history
- 03Search and compare models
- 04Estimate monthly model costs
- 05Find replacement models
Use cases
- 01Monitor new releases and deprecations
- 02Compare candidates against budget and context needs
- 03Estimate monthly API spending
- 04Choose a substitute for a discontinued model
MCP support
It exposes the upstream AI Wave tools through a local MCP server, forwarding calls and returning results from the remote server.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
8Saved tool list · updated 1w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| type | enum | No | Filter by change type: new_model, price_change, api_change, open_source_surge or deprecation.new_model | price_change | api_change | open_source_surge | deprecation |
| limit | number | No | 1-100, default 20 |
| model | string | No | Vendor slug, e.g. claude |
| since | number | No | Unix seconds. Only changes detected after this, oldest first. |
Details
From the project READMEmodelpulse
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 returnedlatestback in),type(new_model,price_change,api_change,open_source_surge,deprecation),model(vendor slug, e.g.claude), andlimit(1-100, default 20).get_model— pricing, context length and catalogue status for one model (id, required: an OpenRouter id such asanthropic/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 ascodingormath, orbuzzfor popularity),outputs,mode,accepts, andlimit(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), frommonthlyMillionTokens(default 10) and aninputSharesplit (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 tohttps://aiwave.elopstudio.com/api/mcp. (The override hook is this rebuild's own labelled reconstruction.)AIWAVE_API_KEY(optional) — when set, every upstream request carriesAuthorization: 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):
npx @reneturcios/modelpulse get_today
npx @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
npx -y @reneturcios/modelpulse mcp
MCP client config:
{
"mcpServers": {
"modelpulse": {
"command": "npx",
"args": ["-y", "@reneturcios/modelpulse", "mcp"]
}
}
}
Docker
docker run -i ghcr.io/cloudcorprecords/modelpulse
docker 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
npm install
npm run build
npm test
npm run dev
The test suite runs entirely against a stubbed JSON-RPC upstream — no live network calls.
License
MIT — see LICENSE.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.