Model Ruler — AI Cost Calculators is answering right now. Last checked 4 min ago. It exposes 12 tools.
Read-only AI/LLM cost tools with current MCP discovery and attributable Registry transport.
Uptime history
20 days of history
20 days agonow
100.0%
Uptime 24h
91 of 91 checks
12
Tools
read from the server
554 ms
Response time
average over 24h
open, no key
Access
streamable-http
Nothing serious here today
Today is the operative word: we check Model Ruler — AI Cost Calculators every 15 minutes and re-read its code on every release. Watch it and you find out the day that stops being true.
Connect this server
Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 4 min ago.
run in your terminal
claude mcp add model-ruler --transport http 'https://modelruler.dev/mcp?dist=model_everywhere_registry_native_official_registry'Settings → Connectors → Add custom connector, then paste this address
https://modelruler.dev/mcp?dist=model_everywhere_registry_native_official_registry~/.codex/config.toml
[mcp_servers.model-ruler]
url = "https://modelruler.dev/mcp?dist=model_everywhere_registry_native_official_registry".cursor/mcp.json
{
"mcpServers": {
"model-ruler": {
"url": "https://modelruler.dev/mcp?dist=model_everywhere_registry_native_official_registry"
}
}
}.vscode/mcp.json
{
"servers": {
"model-ruler": {
"type": "http",
"url": "https://modelruler.dev/mcp?dist=model_everywhere_registry_native_official_registry"
}
}
}Available tools 12
Read directly from the server with tools/list, grouped by what they act on.
If a tool disappears, we record the date.
agent
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.
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 successful multi-step agent task.
automation
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 instead when the question is about LLM inference/reasoning cost per successful agent task.
context
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).
eval
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.
fine
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.
observability
observability-cost-calculator
Use when a user needs to budget LLM observability tooling. Returns monthly cost at given request volume with retention adjustment.
provider
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.
quantization
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.
rag
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.
self
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.
token
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.
Endpoints
| URL | Transport | State | Latency | Checked |
|---|---|---|---|---|
| https://modelruler.dev/mcp?dist=model_everywhere_registry_native_official_registry | streamable-http | answering | 744 ms | 4 min ago |
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Model Ruler — AI Cost Calculators — questions
Answers built from our own checks of this server.
What can Model Ruler — AI Cost Calculators do?
It exposes 12 tools, read directly from the server on our last check. Among them: agent-loop-cost-calculator, agent-workflow-cost-calculator, automation-cost-calculator, context-window-planner, eval-cost-calculator, fine-tune-roi-calculator and 6 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README.
How MCP servers expose tools in the first place →
Is Model Ruler — AI Cost Calculators working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 91 of 91 checks got a reply (100.0%), average response time 554 ms. The bar chart above shows every period we have measured.
How do I connect Model Ruler — AI Cost Calculators?
Copy the ready config from this page. We generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each in the form that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does Model Ruler — AI Cost Calculators need an API key?
No. Model Ruler — AI Cost Calculators completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 12 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is Model Ruler — AI Cost Calculators?
It answers our handshake in 554 ms on average, which is faster than 23% of all working MCP servers we measure. That is on the slow side — worth knowing if the tool sits inside an interactive loop. The comparison comes from our own checks across the whole registry, every 15 minutes.