mcpField
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Modelmeter

Modelmeter

Remote
modelmeter.xyzAI & Agents1Updated 11w ago

About Modelmeter

Modelmeter is a machine-readable, source-verified changelog and pricing record for AI models, built for agents and also available to people through a dashboard. It tracks deprecations, model changes, prices, releases, and historical costs, with cited sources and verification statuses.

Key features

  1. 01Model deprecation and sunset tracking
  2. 02Model and price change events
  3. 03Historical pricing data
  4. 04Cost estimates and model checks
  5. 05Source citations and verification status

Use cases

  1. 01Check whether model dependencies are nearing retirement
  2. 02Estimate costs for a model call
  3. 03Review pricing history and model events

MCP support

The remote MCP server exposes tools for model checks, deprecations, cost estimates, model details, pricing history, and events. It returns the same data as the REST endpoints.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://modelmeter.xyz/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

7
ParameterTypeRequiredDescription
modelstringYesModel id in provider/model form, e.g. anthropic/claude-opus-4-8. Use list_models to discover ids.
input_tokensintegerYesNumber of input (prompt) tokens.
output_tokensintegerYesNumber of output (completion) tokens.

Details

From the project README

Modelmeter

The changelog of record for the AI stack. Live at modelmeter.xyz.

What this is

A machine-readable, source-verified record of everything that changes in the model layer: deprecations with sunset dates and migration targets, silent model swaps behind stable IDs, price changes, releases — plus three years of pricing history and cost tooling. Every event carries its sources with verbatim quotes (most with archive snapshots), a verification status (unverified → human-reviewed verified), and corrections are never silent — a correction event points at what it supersedes.

Built primarily for AI agents to call programmatically, secondarily for humans through a dashboard. Reach it three ways: plain GET URLs returning JSON, a remote MCP server any tool-calling agent can connect to, and a visual dashboard built on the same endpoints. The wedge: clean URLs, stable JSON contracts, an OpenAPI spec, and an llms.txt discoverability file. Most sites in this space are JS-heavy SPAs that agents can't use; Modelmeter is built the other way around.

Endpoints

  • GET /estimate?model=X&input=N&output=M — token cost for a hypothetical call, including upstream-markup comparison for reseller models
  • GET /models — current model catalog, with filters
  • GET /model?id=X — unified card for one model: normalized pricing, capabilities (context window, vision, reasoning, tags), availability, reseller markup, and a price-history summary (launch vs. current price, % change, all-time low/high)
  • GET /check?models=gpt-4o,claude-sonnet-4-6 — is my stack okay? Verdict per model: scheduled retirements with days-remaining and migration targets, past retirements, breaking changes, or a clean bill
  • GET /deprecations — per-model retirement rows: which model dies when, with runway and migration target
  • GET /feed.xml / GET /feed.json — RSS 2.0 / JSON Feed 1.1 of breaking and action-required events
  • GET /events — the changelog of record: deprecations, price changes, launches and surrounding market events, each with severity (breaking | action_required | informational), announced_at/effective_at dates, sources with quotes, and verification status. Filters: provider, type, model, severity, since, until, status
  • GET /history — historical pricing time-series per model, with filters
  • GET /pricing.json — raw pricing snapshot
  • GET /events.json — raw events snapshot
  • GET /history.json — raw historical time-series

See public/openapi.yaml for the full spec.

GitHub Action

Fail CI when a model your repo depends on is deprecated, sunsetting, or already retired:

- uses: modelmeters/modelmeter/actions/check@main
  # with:
  #   models: "gpt-4o, claude-sonnet-4-6"   # optional — otherwise the repo is scanned for known model ids
  #   fail-on: breaking                      # breaking (default) | retired | none
  #   warn-days: "120"                       # sunsets within N days fail; beyond N warn

The action scans your repo for known model ids (catalog + every model named in a deprecation event, dashed and dotted spellings), calls /check, annotates the exact file:line of each affected model, writes a job-summary table with migration targets, and exits nonzero per your fail-on policy.

MCP server

Modelmeter is also a remote MCP server, so any tool-calling agent (Claude, Hermes, …) can call it natively over the Streamable HTTP transport — no auth, no install:

https://modelmeter.xyz/mcp

Tools: check_model_dependencies, list_deprecations, estimate_cost, get_model, list_models, get_price_history, list_events. Start with list_models to discover ids, then get_model or estimate_cost. The MCP tools return the same data as the REST endpoints above — use whichever your runtime prefers.

Datasets

  • pricing/ — current and historical model pricing across providers
  • events/ — the events record (schema 2.0.0): deprecations, price changes, releases, and market events with primary-source citations, verbatim quotes, and verification status

Each entry carries a last_verified date. Daily snapshots in pricing/snapshots/ accumulate the historical record. Wrong pricing is the existential risk — the schema requires explicit human verification before an entry is served.

Contributing

Found a price change? Open a PR updating pricing/current.json with the new value, an updated last_verified date, and the source URL.

Spotted a meaningful AI market event? Add an entry to events/current.json with primary-source citations and verified: true.

Automation opens issues when provider pricing pages change. Pick one up if you want to help.

License

MIT — see LICENSE.

Comments

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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.