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@tokcalc/mcp-server
Advanced tools
tokcalc MCP server — open-source LLM serving capacity planner for AI agents. v0.2.1: stdio + stateless HTTP transport with bearer API key auth + KV-backed rate limiting + public hosted endpoint. 7 read-only tools: estimate_capacity, compare_gpus, recomm
LLM serving capacity planner for AI agents.
Open-source MCP (Model Context Protocol) server that lets AI agents (Cursor, Claude Desktop, Cline) estimate LLM serving capacity — model fit, KV cache, throughput, latency, multi-GPU topology, and cost.
| Tool | What it does |
|---|---|
estimate_capacity | VRAM/KV/throughput/latency/cost for one config |
compare_gpus | Ranked GPU comparison for one workload |
recommend_topology | TP/CP topology recommendation |
estimate_api_vs_self_host | Break-even analysis |
list_models | Discover supported model IDs (35 models) |
list_gpus | Discover supported GPU IDs (30 GPUs) |
All tools are read-only — no side effects, no cloud credentials, no deployments.
Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"tokcalc": {
"command": "npx",
"args": ["-y", "@tokcalc/mcp-server"]
}
}
}
Restart Claude Desktop. The tokcalc server will be available as an MCP tool source.
Add to .cursor/mcp.json in your project:
{
"mcpServers": {
"tokcalc": {
"command": "npx",
"args": ["-y", "@tokcalc/mcp-server"]
}
}
}
Add the same config to Cline's MCP settings.
Ask your AI agent:
"I need to serve Llama 3.3 70B at 32K context for 50 concurrent users. What GPU topology do you recommend, and how much will it cost per month?"
"Compare H100 vs H200 for serving Qwen 2.5 72B in FP8 with continuous batching."
"At what daily request volume does self-hosting Llama 70B on H200 beat the GPT-4o API?"
The agent calls list_models → list_gpus → recommend_topology → estimate_capacity and returns a structured plan with throughput ranges, latency, VRAM, cost, and confidence levels.
Llama 3/3.1/3.3, Llama 4 Scout/Maverick, Mistral 7B, Mixtral 8x7B/8x22B, Mistral Large 3, Pixtral 12B, Codestral, Qwen 2/2.5/3 (incl. MoE + VL), DeepSeek V3/R1/Coder V2, Gemma 2, Phi-3/4, SmolLM2, Falcon 3, OLMo 2, BGE-M3, E5, GTE.
NVIDIA H100/H200/B200/B300, A100, L40S, L4, T4, V100, RTX 4090/3090/5090, RTX PRO 6000 Blackwell, AMD MI300X/MI325X, Intel Gaudi 3, Google TPU v5p/Trillium, Groq LPU, Cerebras CS-3, Apple M2/M3/M4 Ultra/Max.
Apache 2.0 — same as the main tokcalc project.
FAQs
tokcalc MCP server — open-source LLM serving capacity planner for AI agents. stdio + stateless HTTP transport with bearer API key auth + KV-backed rate limiting + public hosted endpoint. 11 read-only tools: estimate_capacity, compare_gpus, recommend_topol
The npm package @tokcalc/mcp-server receives a total of 721 weekly downloads. As such, @tokcalc/mcp-server popularity was classified as not popular.
We found that @tokcalc/mcp-server demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.

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