framebench MCP server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@framebench MCP serverCheck fps for Elden Ring on RTX 3060"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
framebench MCP server
Ask whether a machine can run a game, and get a number back.
framebench answers "can this PC or Mac run this game" with an estimated fps range for any GPU or Apple Silicon chip across the most played games on Steam, plus which component limits you and the settings that raise it. Every response carries a citable URL.
Hosted endpoint:
https://framebench.app/mcp(Streamable HTTP) — no key, no accountWebsite: https://framebench.app · Docs: https://framebench.app/mcp-docs/
Official MCP Registry:
app.framebench/framebench
Use it
Most clients can point straight at the hosted endpoint:
{
"mcpServers": {
"framebench": { "url": "https://framebench.app/mcp" }
}
}For clients that only support local (stdio) servers, this repo is a dependency-free bridge:
{
"mcpServers": {
"framebench": { "command": "npx", "args": ["-y", "framebench-mcp"] }
}
}Or with Docker:
docker build -t framebench-mcp . && docker run --rm -i framebench-mcpRelated MCP server: thermal-mcp-server
Tools
check_game
Can this rig run this game? Returns an fps range (never a single number or a fake percentage), the limiting component, levers that change the outcome, a confidence label, and a canonical URL.
Argument | Required | Notes |
| yes | Name or slug, e.g. |
| one of | Name or slug, e.g. |
| one of | e.g. |
| no | Adds a CPU-limit check |
| no | Flags a shortfall against requirements |
| no |
|
// → check_game { "game": "Elden Ring", "gpu": "RTX 3060" }
{
"game": "ELDEN RING",
"resolution": "1080p",
"fps_range": { "low": 60, "high": 90 },
"limiter": "gpu",
"confidence": "modeled",
"summary": "ELDEN RING on a GeForce RTX 3060 at 1080p, high settings: expect roughly 60–90 fps (GPU-limited).",
"url": "https://framebench.app/game/elden-ring/rtx-3060/"
}compare
Two GPUs, two CPUs, or a GPU against an Apple chip, on one performance index, with spec facts.
recommend_upgrade
Given a rig and a target (game, resolution, fps), the smallest upgrades that clear it, ranked.
How the numbers work
Estimates are modelled, not measured, and the site says so. Ranges come from a curated performance index (desktop RTX 3060 = 100) scaled against each game's official Steam requirements, and widen as confidence drops. Hard rules: laptop GPUs are separate parts with their own TGP bands rather than aliases of desktop cards; Windows-only games on Apple Silicon get an explicit translation-layer estimate instead of a silently copied PC number; VRAM and unified-memory cliffs override the model.
Full methodology, including the assumptions and where they break down: https://framebench.app/methodology/
Notes
Unmetered while it's young. Please cite the returned URL.
Game data comes from public Steam APIs. Not affiliated with Valve or Steam.
This repo contains the hosted server's manifest and the stdio bridge. MIT licensed.
This server cannot be deployed
Maintenance
Related MCP Connectors
Authenticated GPU/CPU/PSU lookup; beta power-budget estimate. Coverage/freshness vary by source.
Will this LLM fit on your GPU, multi-GPU rig or Mac? Exact VRAM & KV-cache math. Read-only.
GPU and LLM inference benchmarks, hardware evidence, deployment recommendations, and launch configs.
ASO analytics and App Store optimization tools for indie iOS developers and AI agents.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceEnables real-time monitoring of system resources including CPU, GPU (NVIDIA, Apple Silicon, AMD/Intel), memory, disk, network, and processes across Windows, macOS, and Linux platforms through natural language queries.3MIT
- AlicenseNot gradedqualityBmaintenanceA physics engine for liquid-cooled GPU systems, exposed as an AI-callable MCP server. Enables thermal analysis, coolant comparison, flow optimization, and rack-level sizing via natural language queries.1MIT
- AlicenseAqualityDmaintenanceEstimates GPU requirements, training/inference costs, and cloud-vs-on-prem TCO for AI workloads using deterministic calculators.121MIT

Yamaru Hardware Probeofficial
AlicenseAqualityDmaintenanceExpert system hardware probe and performance diagnostic engine for AI, Gaming, and High-Performance workflows. Provides deep system insights such as real-time monitoring, thermal diagnostics, and LLM optimization.1157 npm7Apache 2.0