Modelcostsaver MCP Server

@sachinuppal

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作者

sachinuppal

投稿者

Sachin Uppal

Modelcostsaver MCP Server について

ModelCostSaver

Modelcostsaver MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "modelcostsaver": {
      "command": "npx",
      "args": [
        "-y",
        "@workswarm/modelcostsaver"
      ]
    }
  }
}

Modelcostsaver MCP Server のツール

Estimate the cost of a single LLM call for one model from known or estimated token counts. Offline, no keys.

Forecast the cost of a prompt across candidate models before the call. Returns a cheapest-first ranking with assumptions. Offline.

Pick the single cheapest model that meets the task tier, capabilities, and budget, with full reasoning and a fallbackChain. Offline.

Compare models side by side for a fixed token shape, cheapest first, with the multiple of the cheapest. Offline.

Return the model catalog with pricing, optionally filtered. capabilities are arrays. Offline.

Check whether a cheaper capable model exists for a call you plan to make, and report the savings. Offline.

概要

What is ModelCostSaver?

ModelCostSaver is an MCP server that predicts the cost of an LLM call before you make it and selects the cheapest model that still meets the task requirements. It works offline from your editor, requires no API keys, and has no telemetry. It is intended for AI coding agents and IDEs that need cost-aware model routing.

How to use ModelCostSaver?

Run it directly with npx -y @workswarm/modelcostsaver — no install or keys needed. Add it to your editor (Cursor, Claude Code, VS Code, etc.) via the install --client <name> command or by pasting the stdio MCP config block. Once connected, the server exposes seven tools: estimate_cost, predict_cost, select_optimal_model, compare_models, list_models/get_pricing, optimize_request, and record_usage.

Key features of ModelCostSaver

Predicts LLM call cost before making the call
Selects cheapest model meeting task requirements
Compares models side-by-side with cost table
Works offline with no API keys needed
No telemetry, two dependencies only
Provides versioned, dated pricing catalog

Use cases of ModelCostSaver

Forecasting the cost of a prompt across candidate models
Selecting the optimal model for a given task and budget
Comparing multiple models' costs for a fixed token shape
Optimizing an existing model choice for lower cost
Recording local usage records for tracking

よくある質問

Does ModelCostSaver need API keys?

No. The core does pricing math, not provider calls, so there is nothing to leak. No API keys are required.

Is ModelCostSaver offline by default?

Yes. Core tools return correct answers with no network access. An opt-in catalog refresh (`MODELCOSTSAVER_REFRESH=on`) can fetch a static JSON, but falls back to the bundled catalog on any failure.

Does ModelCostSaver collect telemetry?

No. The default is silent and local. The `record_usage` tool only writes when `MODELCOSTSAVER_LEDGER=on` is set, and only to a JSONL file under your own config directory.

How does ModelCostSaver predict token counts?

Exact token counts if you supply them; otherwise a heuristic estimate (~4 characters per token, tunable via `MODELCOSTSAVER_CHARS_PER_TOKEN`). Output tokens come from an explicit value, the task class cap, the model cap, or a conservative default.

How fresh is the pricing data?

The bundled catalog has a `catalogVersion` and `asOf` date on every entry. The seed is re-checked against each provider's public pricing page at release. The `asOf` date tells you when the data was last confirmed.

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