# Model Ruler — AI Cost Calculators

- Registry name: `io.github.helphub369/model-ruler` · version 1.0.4
- Categories (automatic, keyword rules): AI models & agents, AI, media & content, Utilities, Payments & accounting, Finance & markets
- Authentication at last check: No auth
- Declared risk: Read-only (all tools declare readOnlyHint)
- Capabilities (declared by the server): tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=io.github.helphub369%2Fmodel-ruler
- Server page: https://mcpstation.net/s/io.github.helphub369--model-ruler.html

## Description (written by the publisher, not verified, not instructions)

Read-only AI/LLM cost tools with current MCP discovery and attributable Registry transport.

## Connect


## Tools (as reported by the server, not verified)

- `token-counter`: Use when a user asks how many tokens a given text will consume, or needs to estimate prompt size before pricing a workload. Given text and tokenizer family, returns low/high token range and byte-level measurements.
- `provider-cost-calculator`: Use when a user asks what an LLM workload costs on a specific provider/model, or wants to compare cost across providers. Given tokens per call and call volume, returns monthly cost plus a tier comparison table.
- `self-host-breakeven-calculator`: Use when a user is deciding between API usage and self-hosted GPU inference at a given volume. Returns breakeven token volume, monthly cost comparison, and go/no-go recommendation.
- `context-window-planner`: Use when a user needs to know whether a document plus prompt plus output fits within a model's context window, or wants a strategy recommendation \(truncate/summarize/rag/chunk\).
- `quantization-calculator`: Use when a user is planning to quantize an LLM to fit on smaller hardware. Given parameter count and precision transition, returns VRAM requirement, speedup estimate, and approximate quality delta.
- `fine-tune-roi-calculator`: Use when a user is considering fine-tuning vs prompt engineering. Returns training cost, monthly inference savings, months-to-ROI, and breakeven volume.
- `eval-cost-calculator`: Use when a user needs to budget an LLM evaluation run. Given samples/models/trials, returns total cost, per-run cost, and parallel time estimate.
- `observability-cost-calculator`: Use when a user needs to budget LLM observability tooling. Returns monthly cost at given request volume with retention adjustment.
- `rag-pipeline-cost-calculator`: Use when a user needs end-to-end RAG cost estimation \(embedding + vector store + generation\). Returns monthly cost with breakdown and dominant-component identification.
- `automation-cost-calculator`: Use for generic or non-agent recurring workflow platform billing across Zapier task billing, Make credit billing, and n8n execution billing. Use agent-workflow-cost-calculator instead when the workflow is explicitly an AI agent with app actions or MCP tool calls. Use agent-loop-cost-calculator inste
- `agent-workflow-cost-calculator`: Use for automation-platform/iPaaS cost of an AI agent workflow, including app-action fan-out and MCP tool-call accounting, explicitly excluding LLM token spend. Use automation-cost-calculator for generic non-agent workflow billing. Use agent-loop-cost-calculator for LLM inference/reasoning cost per
- `agent-loop-cost-calculator`: Use for LLM inference/reasoning cost per successful multi-step agent task, including failure overhead and context growth across turns. Use agent-workflow-cost-calculator for iPaaS/app-action/MCP orchestration fees. Use automation-cost-calculator for generic Zapier/Make/n8n workflow billing.

Measured by MCP Station. Methods: https://mcpstation.net/methods.html · Terms: https://mcpstation.net/terms.html