Server data from the Official MCP Registry
Estimated game fps for any GPU or Apple Silicon chip, with the limiter and tweaks.
About
Estimated game fps for any GPU or Apple Silicon chip, with the limiter and tweaks.
Remote endpoints: streamable-http: https://framebench.app/mcp
Security Report
Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.
3 tools verified · Open access · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Connect
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"app-framebench-framebench": {
"url": "https://framebench.app/mcp"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
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 account - Website: 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-mcp
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 |
|---|---|---|
game | yes | Name or slug, e.g. "Cyberpunk 2077" |
gpu | one of | Name or slug, e.g. "RTX 3060", "rtx-4060-laptop" |
apple_chip | one of | e.g. "M2 Pro" |
cpu | no | Adds a CPU-limit check |
ram_gb | no | Flags a shortfall against requirements |
resolution | no | 1080p (default), 1440p, 4k |
// → 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.
Reviews
No reviews yet
Be the first to review this server!
More Developer Tools MCP Servers
Fetch
Freeby Modelcontextprotocol · Developer Tools
Web content fetching and conversion for efficient LLM usage
Git
Freeby Modelcontextprotocol · Developer Tools
Read, search, and manipulate Git repositories programmatically
Toleno
Freeby Toleno · Developer Tools
Toleno Network MCP Server — Manage your Toleno mining account with Claude AI using natural language.
mcp-creator-python
Freeby mcp-marketplace · Developer Tools
Create, build, and publish Python MCP servers to PyPI — conversationally.
MCP Marketplace
Freeby mcp-marketplace · Developer Tools
Search and install MCP servers from inside your AI client.
MarkItDown
Freeby Microsoft · Content & Media
Convert files (PDF, Word, Excel, images, audio) to Markdown for LLM consumption
