@tokcalc/mcp-server

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.

Tools

| 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 (39 models) | | list_gpus | Discover supported GPU IDs (30 GPUs) | | get_mlperf_benchmarks | Curated MLPerf v4.1 reference configs | | find_config_for_slo | Inverse planner — SLOs + traffic → feasible configs ranked by cost/throughput/value | | plan_deployment | One-call decision brief — memory + perf + build-vs-buy + risks + next steps | | fetch_model_spec | Diff the catalog against live HuggingFace config.json (24h cache) | | record_measured | Store real tok/s measurements; future estimates self-calibrate |

All tools are read-only — no side effects, no cloud credentials, no deployments.

Install

Claude Desktop

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.

Cursor

Add to .cursor/mcp.json in your project:

{
  "mcpServers": {
    "tokcalc": {
      "command": "npx",
      "args": ["-y", "@tokcalc/mcp-server"]
    }
  }
}

Cline (VS Code)

Add the same config to Cline's MCP settings.

Example prompts

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.

Supported models (35)

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.

Supported GPUs (30)

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.

License

Apache 2.0 — same as the main tokcalc project.

Links

• Live calculator
• GitHub
• CONTRIBUTING