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by io.github.edushinka
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Predictions feed (licensed)

get_predictions
Read-onlyIdempotent

The licensed Predictions feed for one league: kind=games (our line, total, win chances and fair prices for every game), kind=props (player projections with over and under chances) or kind=markets (team totals, halves, quarters and other game markets). Needs a Predictions licence covering the league. League codes: nfl, cfb, nba, cbb, wnba, nhl, mlb, afl, nrl, epl, laliga, bundesliga, seriea, ligue1, mls, eflchamp, brasileirao, aleague, worldcup, tennis, ufc, cricket.

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": {
      "league": "<league>"
    },
    "name": "get_predictions"
  }
}

Input Schema

NameRequiredDescriptionDefault
kindNogames (default), props or markets.
teamNoOptional: only rows whose teams (or player's team or name) contain this text.
limitNoMost rows to return (default 50).
leagueYesLeague code, for example nfl, nba, epl.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already cover readOnly/idempotent/non-destructive, so credit is for added context: the licence prerequisite, the league-scoped nature, and what each kind yields. It leaves out rate limits and pagination behavior, but the licensing caveat is a genuinely useful behavioral disclosure the annotations cannot express.

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

Conciseness4/5

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

Front-loaded with the resource and the three kind variants, then the licensing constraint. The long league-code list is bulky but practically useful; everything else earns its place.

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?

For a read-only feed with no output schema, the description gives enough about the shape of returned data per kind and the access prerequisite to call it correctly. Pagination/limit behavior is left unstated, a minor gap given the schema documents it.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3; the description earns more by explaining what each kind enum value actually returns (lines/totals/win chances vs player projections vs other game markets), which the schema only names. The team and limit parameters are left entirely to the schema.

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

Purpose4/5

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

States a specific resource (licensed Predictions feed) scoped to one league and enumerates the three data shapes it can return, so the agent knows exactly what data comes back. It does not explicitly contrast with near siblings like get_free_picks or get_predicted_scores, so it falls short of a 5.

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?

The licence requirement is stated ('needs a Predictions licence covering the league'), which implies entitlement gating, but there is no explicit when-to-use-this-vs-alternatives guidance despite several similarly named prediction/picks siblings. Usage is inferred rather than directed.

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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