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Sports Probabilities MCP

by io.github.commodus67
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Team probabilities

get_team_probabilities

Title, playoff, division, seeding or relegation probabilities for one team, with its current record and projected finish. Backed by a Monte Carlo simulation of every remaining game on the real schedule.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "sport": "<sport>",
      "team": "<team>"
    },
    "name": "get_team_probabilities"
  }
}

Input Schema

NameRequiredDescriptionDefault
teamYesTeam name, city or abbreviation, for example "Chiefs", "KC" or "Arsenal".
sportYesnfl, mlb, nhl, nba or soccer.
leagueNoSoccer only. A league name or ESPN slug, for example "Premier League" or "esp.1". Ignored for every other sport.
seasonNoNFL, MLB, NHL and NBA only. A past season year, for backtesting. NHL and NBA seasons are named by the year they end, so 2027 means 2026-27.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations supplied, the description carries the full burden and does disclose the derivation method: a Monte Carlo simulation of every remaining game on the real schedule, which tells an agent these are model estimates rather than stated facts. It also previews the returned fields (current record, projected finish). It stops short of stating read-only/no-side-effect behavior or how it handles an unknown team or an out-of-range season.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, both earning their place: the first front-loads scope and outputs, the second justifies the numbers. No filler, no restatement of the title.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description usefully names what is returned (current record, projected finish) and where the numbers come from. For a four-parameter lookup with fully documented schema fields this is nearly sufficient; only error/edge behavior and explicit sibling routing are absent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with team format, the sport enum, the soccer-only league field and the season backtesting rule all documented in the schema. The description adds no parameter guidance of its own beyond implying a single team, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb+resource: probabilities (title, playoff, division, seeding, relegation) for ONE team, plus the current record and projected finish. The 'one team' scoping implicitly distinguishes it from the league-wide sibling, and the enumerated probability types tell an agent exactly what comes back.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied rather than stated: 'for one team' suggests this is the single-team view versus a league-wide one, but no sibling (get_league_probabilities, compare_model_vs_market, run_scenario) is named and no when-not condition is given. An agent must infer the selection rule from the scope phrase alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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