Model Ruler — AI Cost Calculators
REMOTE · MODELRULER.DEV
Read-only AI/LLM cost tools with current MCP discovery and attributable Registry transport.
Available components
Last scanned (12 hours ago)
Scores higher than 66% of remote servers
Reachable. Last reached 23 Oct 2026.
Category AI and Machine Learning
How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. How we score → Why this is hard to score →
Endpoint Security63
- The endpoint's TLS certificate is valid, in date, and uses a strong key. View diagnostics → Pass
- No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one. See how to fix → View diagnostics → Partial
- HTTPS enforcement could not be verified: the plaintext port answered with HTTP 405, which proves neither a plaintext path nor enforcement. View diagnostics → Unverified
- HSTS check failed: the Strict-Transport-Security header is absent. See how to fix → View diagnostics → Fail
- DNSSEC check failed: this domain isn't protected by DNSSEC. See how to fix → View diagnostics → Fail
Transport & Reachability100
- Verified streamable-http transport via a live MCP handshake. View diagnostics → Pass
Schema Quality & AI Usability83
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 2015 tokens (~167/item across 12 items; 12 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Tools include usage examples.Pass
Stability & Change Management53
- Stability observed for 16 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety75
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- 0 of 1 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation; "eval-cost-calculator" implies "eval" and declares readOnlyHint instead, contradicting what its own name says it does. See how to fix → Fail
- An AI judge read all 13 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 22 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 47 to 50. That category is still filling its 30-day observation window: 14 days of observed history at the previous scan, 15 at this one. The score rises as the window fills, whether or not the server changes.
- 20 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 40 to 43. That category is still filling its 30-day observation window: 12 days of observed history at the previous scan, 13 at this one. The score rises as the window fills, whether or not the server changes.
- 18 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 33 to 37. That category is still filling its 30-day observation window: 10 days of observed history at the previous scan, 11 at this one. The score rises as the window fills, whether or not the server changes.
- 16 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 27 to 30. That category is still filling its 30-day observation window: 8 days of observed history at the previous scan, 9 at this one. The score rises as the window fills, whether or not the server changes.
- 14 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 20 to 23. That category is still filling its 30-day observation window: 6 days of observed history at the previous scan, 7 at this one. The score rises as the window fills, whether or not the server changes.
- 12 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 10 to 17. That category is still filling its 30-day observation window: 3 days of observed history at the previous scan, 5 at this one. The score rises as the window fills, whether or not the server changes.
- 9 Oct 26 +1
- Stability: unverified → 0.07 ▲ functional
- 7 Oct 26 67
First indexed and scored.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 23 Oct 2026 · Probed https://modelruler.dev/mcp?dist=model_everywhere_registry_native_official_registry
TLS valid
Negotiated TLS 1.3 with TLS_AES_128_GCM_SHA256 .
| Subject | Issuer | Valid from | Valid until | Key | Signature | Serial |
|---|---|---|---|---|---|---|
| CN=modelruler.dev | CN=WE1,O=Google Trust Services,C=US | 27 Sept 2026 | 26 Dec 2026 | ECDSA 256 | ECDSA-SHA256 | a73715fba800d2080e9308d4770c4263 |
| SANs: modelruler.dev, *.modelruler.dev | ||||||
| CN=WE1,O=Google Trust Services,C=US (CA) | CN=GTS Root R4,O=Google Trust Services LLC,C=US | 13 Dec 2023 | 20 Feb 2029 | ECDSA 256 | ECDSA-SHA384 | 7ff31977972c224a76155d13b6d685e3 |
| CN=GTS Root R4,O=Google Trust Services LLC,C=US (CA) | CN=GlobalSign Root CA,OU=Root CA,O=GlobalSign nv-sa,C=BE | 15 Nov 2023 | 28 Jan 2028 | ECDSA 384 | SHA256-RSA | 7fe530bf331343bedd821610493d8a1b |
Background: What to check on a remote MCP endpoint →
DNSSEC insecure
Validation of modelruler.dev. — Not signed
| Zone | DS | Keys | Algorithms | Outcome |
|---|---|---|---|---|
| . | trust_anchor | 20326, 38696 | 8, 8 | Verified |
| dev. | present | 60074 | 8 | Verified |
| modelruler.dev. | absent | Unsigned (proven) parent-signed NSEC/NSEC3 proves an unsigned delegation |
Authentication No authorisation required
The endpoint answered without asking for a token. Anyone who knows the URL can reach it.
| Result | No authorisation required |
|---|---|
| HTTP status | 200 |
Background: How OAuth 2.1 works in the 2026 MCP spec →
Transports 2 probes
| Transport | URL | Outcome | Status | Location |
|---|---|---|---|---|
| streamable-http | https://modelruler.dev/mcp?dist=model_everywhere_registry_native_official_registry | Verified | 200 | |
| http (plaintext) | http://modelruler.dev/mcp?dist=model_everywhere_registry_native_official_registry | Inconclusive | 405 |
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
agent-loop-cost-calculator Agent LLM Loop Cost Calculator ~243
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.
| Name | Type | Req | Description |
|---|---|---|---|
| avg_tokens_in_per_turn | number | – | Avg input tokens per turn (default 3000) |
| avg_tokens_out_per_turn | number | – | Avg output tokens per turn (default 400) |
| context_growth_factor | number | – | Multiplier on input tokens as conversation grows (default 1.4) |
| model | string | – | LLM model (default claude-sonnet-4-6) |
| provider | string | – | LLM provider (default anthropic) |
| steps_per_task | number | – | Avg reasoning steps per task (default 5) |
| success_rate | number | – | Task success rate 0-1 (default 0.7) |
| tasks_per_month | number | yes | Tasks attempted per month |
| tool_calls_per_step | number | – | Avg tool invocations per step (default 2) |
No output schema declared.
{"tasks_per_month":60000}agent-workflow-cost-calculator Agent Workflow Platform Cost Calculator ~158
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.
| Name | Type | Req | Description |
|---|---|---|---|
| agent_runs_per_month | number | yes | Agent workflow runs per month |
| app_actions_per_run | number | – | Downstream app actions per agent run (default 3) |
| make_modules_per_run | number | – | Make modules per run; defaults to app actions + MCP calls |
| mcp_tool_calls_per_run | number | – | MCP tool calls per agent run (default 1) |
No output schema declared.
{"agent_runs_per_month":20000}automation-cost-calculator Workflow Automation Platform Cost Calculator ~146
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.
| Name | Type | Req | Description |
|---|---|---|---|
| billable_steps_per_run | number | – | Billable actions/modules per run (default 5) |
| make_modules_per_run | number | – | Make module actions per scenario run; defaults to billable_steps_per_run |
| workflow_runs_per_month | number | yes | Workflow/scenario runs per month |
No output schema declared.
{"workflow_runs_per_month":120000}context-window-planner Context Window Planner ~121
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).
| Name | Type | Req | Description |
|---|---|---|---|
| doc_tokens | number | yes | Primary document/content tokens |
| expected_out_tokens | number | – | Reserved output budget (default 1000) |
| model_context_window | number | yes | Target model context size |
| overhead_tokens | number | – | System prompt + few-shot + history (default 2000) |
| strategy_hint | string | – | Preferred strategy (optional) |
No output schema declared.
{"doc_tokens":18000,"model_context_window":200000}eval-cost-calculator Evaluation Cost Calculator ~205
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.
| Name | Type | Req | Description |
|---|---|---|---|
| avg_tokens_in | number | – | Avg input tokens per sample (default 2000) |
| avg_tokens_out | number | – | Avg output tokens per sample (default 500) |
| judge_enabled | boolean | – | Enable LLM-as-judge second pass (default false) |
| judge_tokens_in | number | – | Judge input tokens (default 1500) |
| judge_tokens_out | number | – | Judge output tokens (default 200) |
| model | string | – | Eval model (default claude-sonnet-4-6) |
| models | number | – | Candidate models (default 1) |
| provider | string | – | Eval provider (default anthropic) |
| samples | number | yes | Number of eval samples |
| trials_per_sample | number | – | Repeats per sample (default 1) |
No output schema declared.
{"samples":2000}fine-tune-roi-calculator Fine-Tune ROI Calculator ~163
Use when a user is considering fine-tuning vs prompt engineering. Returns training cost, monthly inference savings, months-to-ROI, and breakeven volume.
| Name | Type | Req | Description |
|---|---|---|---|
| base_inference_cost_1m | number | – | Baseline API output cost per 1M |
| finetuned_inference_cost_1m | number | – | Fine-tuned inference cost per 1M |
| monthly_inference_tokens | number | yes | Expected monthly inference volume (output tokens) |
| prompt_reduction_pct | number | – | Prompt size reduction % from eliminating few-shot (default 0) |
| train_cost_per_1m | number | – | Training cost per 1M tokens |
| train_tokens | number | yes | Training tokens (dataset × epochs) |
No output schema declared.
{"monthly_inference_tokens":300000000,"train_tokens":50000000}observability-cost-calculator LLM Observability Cost Calculator ~95
Use when a user needs to budget LLM observability tooling. Returns monthly cost at given request volume with retention adjustment.
| Name | Type | Req | Description |
|---|---|---|---|
| avg_log_bytes | number | – | Avg payload bytes per traced request (default 4096) |
| provider | string | yes | Observability provider |
| requests_per_day | number | yes | Average daily LLM requests |
| retention_days | number | – | Retention in days (default 30) |
No output schema declared.
{"provider":"langsmith","requests_per_day":50000}provider-cost-calculator LLM Provider Cost Calculator ~145
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.
| Name | Type | Req | Description |
|---|---|---|---|
| calls_per_month | number | – | Monthly call volume |
| include_comparison | boolean | – | Include tier comparison table (default true) |
| model | string | – | Target model (e.g. claude-sonnet-4-6) |
| provider | string | – | Target provider (e.g. anthropic, openai, together) |
| tokens_in | number | yes | Input tokens per call |
| tokens_out | number | yes | Output tokens per call |
No output schema declared.
{"calls_per_month":100000,"tokens_in":1200,"tokens_out":400}quantization-calculator Quantization Calculator ~124
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.
| Name | Type | Req | Description |
|---|---|---|---|
| batch_size | number | – | Serving batch size (default 1) |
| kv_cache_tokens | number | – | Max KV cache tokens (default 8192) |
| params_billions | number | yes | Model parameter count in billions |
| precision_from | string | – | Starting precision (default bf16) |
| precision_to | string | – | Target precision (default int4) |
No output schema declared.
{"params_billions":8}rag-pipeline-cost-calculator RAG Pipeline Cost Calculator ~215
Use when a user needs end-to-end RAG cost estimation (embedding + vector store + generation). Returns monthly cost with breakdown and dominant-component identification.
| Name | Type | Req | Description |
|---|---|---|---|
| answer_tokens | number | – | Avg answer tokens (default 400) |
| chunk_size_tokens | number | – | Avg chunk size (default 512) |
| chunks_retrieved | number | – | Chunks per query (default 5) |
| corpus_tokens | number | yes | Indexed corpus size in tokens |
| embedding_provider | string | – | Embedding model |
| generator_model | string | – | Generator model (default claude-sonnet-4-6) |
| generator_provider | string | – | Generator LLM provider (default anthropic) |
| queries_per_day | number | yes | User query volume per day |
| question_tokens | number | – | Avg question tokens (default 100) |
| reindex_fraction_per_month | number | – | Fraction of corpus re-embedded per month (default 0.1) |
| vector_store | string | – | Vector store |
No output schema declared.
{"corpus_tokens":25000000,"queries_per_day":5000}self-host-breakeven-calculator Self-Host Breakeven Calculator ~161
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.
| Name | Type | Req | Description |
|---|---|---|---|
| api_cost_per_1m_out | number | – | Current API output cost per 1M tokens |
| gpu_provider | string | – | GPU provider (e.g. runpod, modal) |
| gpu_type | string | – | GPU type (e.g. h100, a100-80gb) |
| monthly_tokens | number | yes | Monthly output token volume |
| operational_overhead_pct | number | – | Ops overhead % on GPU cost (default 40) |
| utilization_pct | number | – | Expected GPU utilization % (default 60) |
No output schema declared.
{"monthly_tokens":750000000}token-counter Token Counter ~91
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.
| Name | Type | Req | Description |
|---|---|---|---|
| expected_out_tokens | number | – | Expected output token budget (optional) |
| text | string | yes | Text content to count tokens for |
| tokenizer | string | – | Tokenizer family (default: default) |
No output schema declared.
{"text":"Summarize the Q3 board deck into five concise bullet points for the exec team."}How do I install the Model Ruler — AI Cost Calculators MCP server?
Model Ruler — AI Cost Calculators is a hosted endpoint at https://modelruler.dev/mcp?dist=model_everywhere_registry_native_official_registry, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
remote · modelruler.dev
claude mcp add --transport http helphub369-model-ruler 'https://modelruler.dev/mcp?dist=model_everywhere_registry_native_official_registry'
What is the Model Ruler — AI Cost Calculators MCP server?
Model Ruler — AI Cost Calculators is an MCP server listed in the public MCP registry as io.github.helphub369/model-ruler. Read-only AI/LLM cost tools with current MCP discovery and attributable Registry transport. This page covers its hosted endpoint (https://modelruler.dev/mcp?dist=model_everywhere_registry_native_official_registry).
Is the Model Ruler — AI Cost Calculators MCP server safe to use?
Model Ruler — AI Cost Calculators scores 74 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the Model Ruler — AI Cost Calculators MCP server expose?
Model Ruler — AI Cost Calculators exposes 12 tools: token-counter, provider-cost-calculator, self-host-breakeven-calculator, context-window-planner, quantization-calculator, and 7 more. Their descriptions and schemas cost roughly 1,867 tokens of context every time the server is loaded.
Does the Model Ruler — AI Cost Calculators MCP server require authentication?
No. We connected to Model Ruler — AI Cost Calculators without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.
Is the Model Ruler — AI Cost Calculators MCP server still maintained?
Model Ruler — AI Cost Calculators is still listed as active in the MCP registry. We last reached this channel on 23 October 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.