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rankedtoday-mcp

rankedtoday-mcp

by RyanKramer
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Python
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Ranked Today MCP

League of Legends math and cognitive-readiness reference for AI agents. Estimate MMR from LP flow, project a climb, calculate KDA and lifetime hours, and get the research on sleep and reaction time without the folklore.

No API key. No Riot account. No network calls. Every tool is a pure function and every estimate shows its arithmetic.

Web versions: shouldiplayrankedtoday.com/tools

Install

{
  "mcpServers": {
    "rankedtoday": {
      "command": "uvx",
      "args": ["rankedtoday-mcp"]
    }
  }
}

Or pip install rankedtoday-mcp.

Related MCP server: MCP Learning

Tools

Tool

What it does

estimate_mmr

Infers whether MMR sits above or below your rank from LP gain/loss asymmetry

climb_calculator

Games required to reach a target LP total, or whether the climb runs backward

kda

KDA with a role-aware read and the winrate caveat

hours_played

Lifetime hours from account level, with the math and the error bars

hardware_latency_budget

How much of a reaction-test score is your monitor and mouse, not you

readiness_reference

What the sleep/cognition research supports — and explicitly what it does not

season_countdown

Days to a ranked season boundary

rankedtoday_tools

Free cognitive tests and League tools on the web

Design notes

Nothing here pretends to read hidden data. Riot removed public MMR from the API years ago, and there is no lifetime hours counter. estimate_mmr infers from the LP exchange rate and says so; hours_played converts account level and reports plus-or-minus 20% along with what it cannot see. Any tool printing an exact MMR from a summoner name is guessing without telling you.

readiness_reference includes the disconfirming evidence. It lists what the research supports (+49 ms after one all-nighter, 5x more attention lapses, the 516-player rank correlation) and what it does not — including a controlled study where sleep-deprived players' cognition tanked while game outcomes barely moved. Readiness is a probabilistic personal signal, never a win predictor.

climb_calculator will tell you the climb runs backward. Below your breakeven winrate, more games means less LP. It returns that verdict rather than a games-required number, because the honest answer is that this climb does not finish.

Development

git clone https://github.com/RyanKramer/rankedtoday-mcp
cd rankedtoday-mcp
pip install -e .
rankedtoday-mcp

About

Built by Ranked Today — a 3-minute daily cognitive readiness check for League players. Free cognitive tests, no signup.

Not affiliated with or endorsed by Riot Games.

MIT licensed.

Available Tools

8 tools
climb_calculatorB

How many games a climb takes at a given winrate, or whether it runs backward.

LP totals are cumulative: one division is 100 LP, one tier is 400 LP. So Gold IV 0 LP to Plat IV 0 LP is 400.

Inputs: winrate_pct (required), target_lp_total (required), current_lp_total (required), lp_per_win, +1 more. Returns: object

estimate_mmrA

Estimate where your MMR sits relative to your rank, from LP flow.

Riot does not expose MMR and no site can read it. What MMR does leak is the exchange rate: when MMR matches rank, wins and losses move you roughly symmetrically. Asymmetry is the signal.

Inputs: lp_gain (required), lp_loss (required), current_rank. Returns: object

hardware_latency_budgetA

Estimate how much of a reaction-test score is equipment rather than you.

A reaction test measures the whole chain: the screen has to draw the stimulus and the input device has to report the response. Only the middle is you, and the rest is worth real milliseconds.

Inputs: refresh_hz, is_touchscreen, mouse_polling_hz. Returns: object

hours_playedA

Estimate lifetime hours from account level, with the math shown.

Levels are earned almost entirely by finishing games, so level is the only signal that reflects a whole account history. Expect about 20% error.

Inputs: account_level (required), minutes_per_game. Returns: object

kdaC

Calculate KDA with an honest, role-aware read.

Inputs: kills (required), deaths (required), assists (required), role. Returns: object

rankedtoday_toolsC

Free tools and cognitive tests on the web.

Inputs: no named parameters. Returns: object

readiness_referenceB

What the research actually supports about cognition and gaming performance.

Included because these numbers get misquoted constantly in both directions.

Inputs: no named parameters. Returns: object

season_countdownA

Days remaining until a ranked season boundary or any other date.

Pass from_date (YYYY-MM-DD) to compute from a fixed day; otherwise uses today.

Inputs: target_date (required), from_date. Returns: object

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    B3.2/5.0

    Scored across 8 tools

    Disambiguation4/5

    Most tools have clearly distinct purposes: KDA, MMR estimation, climb calculation, hours played, latency, and countdown are all separate concerns. However, 'rankedtoday_tools' is vague and could be confused with 'readiness_reference' since both are informational rather than computational.

    Naming Consistency3/5

    Tool names mix patterns: some are verb_noun (estimate_mmr, climb_calculator), some are plain nouns (kda, season_countdown), and some are adjective_noun (readiness_reference, hardware_latency_budget). The style is mostly snake_case but lacks a consistent verb_noun or noun convention, making naming somewhat inconsistent.

    Tool Count5/5

    The server has 8 tools, which is a well-scoped number for a gaming analytics and utility server. Each tool earns its place by covering a distinct calculation or reference need, without bloating the set.

    Completeness4/5

    The tools cover core player needs: KDA, MMR, climb, hours, latency, and season countdown. Minor gaps exist, such as a direct rank conversion or winrate calculator, but these are not obvious dead ends and the overall domain of ranked gaming analytics is well covered.

    Resources

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