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TipMaster Expert Soccer Predictions

by net.tipmaster
OverviewDiscussionsAdmin

How often the experts are right

get_accuracy
Read-onlyIdempotent

How often TipMaster players got football (soccer) matches right, per group (Top 20%, Top 25%, All players): majorityCorrect is the share of matches where the group’s most common home/draw/away call was right, outcomeCorrect the share of individual predictions with the right outcome, exactCorrect the share with the exact final score (National only). National covers the last 8 weeks, each week’s groups ranked on the 8 weeks before it; Classic covers this season, ranked by career record since 1997, and career gives each group’s average career points per match since 1997. A group’s rates appear once it has 50 matches with a published split (reason too_few_matches until then). Pass competition (National league name) or game (Classic) to narrow it.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_accuracy"
  }
}

Input Schema

NameRequiredDescriptionDefault
gameNoClassic game: btm = Bundesliga TipMaster, tmi = TipMaster International
modeNonational (exact scores, 15 countries) or classic (home/draw/away, 25 matches a week); omit for both.
competitionNoNational league name, e.g. "Bundesliga"

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.9/5.0
Behavior4/5

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

Beyond the readOnly/idempotent annotations, the description discloses real behavioral detail: rates only appear after 50 matches, a too_few_matches reason is returned before that, and National is a rolling 8-week window ranked on the prior 8 weeks. It does not discuss payload size, pagination or caching, but the data-availability semantics are unusually well covered.

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?

Purpose is front-loaded and every clause carries substantive information about metrics, windows or thresholds. It is dense — long compound sentences pack metric definitions, ranking windows and the minimum-match rule together — so it reads heavier than it needs to, but there is essentially no filler.

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

Completeness5/5

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 must define the return fields, and it does: majorityCorrect, outcomeCorrect and exactCorrect are each defined, including the exactCorrect National-only caveat. Combined with the availability rule and filter guidance, an agent has enough to call and interpret the tool correctly.

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 the baseline would be 3, but the description adds cross-parameter meaning the schema lacks: 'competition' is the National league name and 'game' applies to Classic, making the mode pairing explicit. That interaction constraint is genuinely useful context an agent would otherwise have to infer from enum descriptions.

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?

The opening clause states exactly what is returned: per-group accuracy rates for TipMaster football predictions, with the three metrics named (majorityCorrect, outcomeCorrect, exactCorrect). It is clearly an aggregate-accuracy tool rather than a pick or match lookup, so it is distinguishable from siblings like get_expert_picks and get_consensus by intent, though no sibling is named explicitly.

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 prescribed: it explains the National vs Classic windows and says 'Pass competition ... or game ... to narrow it', which tells the agent how to filter but not when this tool is preferable to get_consensus or get_match_prediction. No exclusions or preconditions for choosing among siblings are given.

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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