mcsr-mcp
A Model Context Protocol server that gives AI assistants real-time access to the MCSR Ranked competitive Minecraft speedrunning API, enabling plain-language queries about players, matches, stats, and more.
README
<p align="center"> <img src="assets/banner.jpg" alt="MCSR Ranked MCP" width="100%" /> </p>
<p align="center"> <a href="https://github.com/miyakejima/mcsr-mcp/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="MIT License" /></a> <a href="https://nodejs.org"><img src="https://img.shields.io/badge/node-%3E%3D18-brightgreen.svg" alt="Node.js >= 18" /></a> <a href="https://modelcontextprotocol.io"><img src="https://img.shields.io/badge/MCP-compatible-blueviolet.svg" alt="MCP Compatible" /></a> <a href="https://mcsrranked.com"><img src="https://img.shields.io/badge/data-mcsrranked.com-orange.svg" alt="MCSR Ranked" /></a> </p>
<br />
mcsr-mcp is a Model Context Protocol server that gives AI assistants real-time access to the MCSR Ranked competitive Minecraft speedrunning API.
Ask your AI anything about any ranked player — form analysis, personal bests, ELO history, pre-match scouting, rivals, decay status — in plain language. No commands, no dashboards, no manual API calls.
"Scout v_strid before my match"
"What are Feinberg's top 10 fastest runs this season?"
"Has v_strid been tilting? Show me their ELO history"
"When does my rank decay?"
"Who are v_strid's rivals and where are they weakest?"
How it looks
<p align="center"> <img src="assets/example-output.jpg" alt="Example tool output" width="80%" /> </p>
What it can do
<p align="center"> <img src="assets/tools-diagram.jpg" alt="Tools overview" width="90%" /> </p>
All 18 tools
| Tool | What it does |
|---|---|
get_player |
Profile, ELO, rank, tier, stats, Twitch/YouTube links |
get_player_matches |
Raw match history with seed and bastion data |
get_player_seasons |
Season-by-season performance history |
get_versus |
Head-to-head record between two players |
get_versus_matches |
Full H2H match list |
get_leaderboard |
Global ranked standings |
get_match |
Single match details by ID |
get_weekly_race |
Weekly race leaderboard |
analyze_player_form |
Win rates by seed type, bastion type, opponent ELO bracket |
get_rank_context |
Leaderboard neighborhood + ELO gaps to milestone ranks |
scout_opponent |
Pre-match scouting report with Twitch VOD timestamps |
compare_players |
Side-by-side player comparison with H2H |
find_rivals |
Opponents a player consistently loses to |
get_personal_bests |
Fastest ranked completions, filterable by seed type |
get_elo_history |
Full ELO trajectory — peak, trough, streaks, per-match deltas |
get_decay_status |
Exact decay date + urgency level (✅ SAFE → 🚨 CRITICAL) |
analyze_session_form |
Per-session stats grouped by playtime gaps |
get_capabilities |
Ask your AI "what can this MCP do?" to get the full guide |
Installation
Requirements
- Node.js v18 or later
- An MCP-compatible AI client (see below)
1. Clone and build
git clone https://github.com/miyakejima/mcsr-mcp.git
cd mcsr-mcp
npm install
npm run build
The compiled server will be at dist/index.js.
2. Add to your AI client
Add the following block to your MCP config file:
{
"mcpServers": {
"mcsr": {
"command": "node",
"args": ["/absolute/path/to/mcsr-mcp/dist/index.js"]
}
}
}
Config file locations
| Client | Config file |
|---|---|
| Antigravity | ~/.gemini/config/mcp_config.json |
| Claude Desktop (macOS) | ~/Library/Application Support/Claude/claude_desktop_config.json |
| Claude Desktop (Windows) | %APPDATA%\Claude\claude_desktop_config.json |
| Cursor | .cursor/mcp.json (project) or ~/.cursor/mcp.json (global) |
| Windsurf | ~/.codeium/windsurf/mcp_config.json |
Windows path example:
{
"mcpServers": {
"mcsr": {
"command": "node",
"args": ["C:/Users/yourname/mcsr-mcp/dist/index.js"]
}
}
}
3. Restart your client
After saving, restart your AI client. The mcsr server will appear with all 18 tools enabled. You can verify by asking:
"What can the mcsr MCP do?"
Usage examples
Pre-match scouting
"Scout v_strid — I'm miyakejima"
Returns: recent form, seed/bastion win rates, weaknesses, your H2H record, and Twitch VOD links with timestamps so you can jump straight to their runs.
Form analysis
"What are Feinberg's weaknesses over the last 30 matches?"
Returns: win rate by every seed type and bastion variant, ELO bracket performance, streak data.
Personal bests
"Show me v_strid's top 5 fastest Buried Treasure runs"
Returns: ranked list of fastest completions for that seed, with opponent, date, and VOD timestamp link.
ELO history
"Has v_strid been tilting lately? Show me their last 50 matches"
Returns: full ELO timeline, peak/trough, total delta, longest win and loss streaks.
Decay tracker
"When does my rank decay? I'm miyakejima"
Returns: exact date, days/hours remaining, urgency level, last match date.
Rivals
"Who does Feinberg consistently lose to?"
Returns: opponents with a net losing record, sorted by ELO proximity and net loss count.
Notes
- No API key required. Uses the public MCSR Ranked REST API at
api.mcsrranked.com. - Rate limit: The API allows ~500 requests per 10 minutes. A 150ms delay between requests is applied automatically.
- Loss times are unavailable. The MCSR Ranked API only records the winner's finish time — this is an upstream API limitation.
- VOD links include Twitch timestamps to jump directly to the run start.
- Season filtering: Add "season 10" (or any number) to any query to pull historical season data.
- Seed type filter on
get_personal_bests:RUINED_PORTAL,SHIPWRECK,DESERT_TEMPLE,VILLAGE,BURIED_TREASURE.
Data source
All data is from the official MCSR Ranked API. This project is not affiliated with or endorsed by MCSR Ranked.
License
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