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by io.github.edushinka
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Predicted scores

get_predicted_scores
Read-onlyIdempotent

The model's projected final score, margin and total for every upcoming game in a league. Free tier: the public feed (home win chance for the next 3 games only). With a Predictions licence for the league: every game with home, away and draw chances. League codes: nfl, cfb, nba, cbb, wnba, nhl, mlb, afl, nrl, epl, laliga, bundesliga, seriea, ligue1, mls, eflchamp, brasileirao, aleague, worldcup, tennis, ufc, cricket (also ncaaf, ncaab, soccer).

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_predicted_scores"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMost games to return (default 40).
leagueYesLeague code, for example nfl, nba, epl, cfb. 'soccer' = every soccer league (free feed).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety bar is low. The description adds genuinely useful context beyond them: a paywall/entitlement gating model that changes the shape and volume of results depending on licence, plus explicit coverage of the free-tier feed.

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-loads what is returned, then tier behavior, then valid codes — a sensible ordering with little wasted prose. The long league-code list is dense but earns its place by preventing invalid calls.

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?

No output schema exists, so the description carries the burden of explaining return values, and it does so (score, margin, total; win/draw chances). The main remaining gap is the relationship to sibling prediction tools.

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%, so both parameters are already documented and the baseline is 3. The description adds a full league-code enumeration beyond the schema's terse examples, including the 'soccer' aggregation alias, which compensates somewhat but is already partially mirrored in 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 verb+resource: projected final score, margin and total for every upcoming game in a league. Very concrete about the returned fields, but never distinguishes itself from siblings like get_predictions or get_free_picks, which sound overlapping.

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?

Explains the access tiers (free feed = home win chance for next 3 games; licence = all games with home/away/draw chances), which helps an agent judge whether the call will be useful. However, it never says when to prefer this over get_predictions or get_free_picks, so the choice among siblings is left to inference.

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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