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aiballfooty

AI Ball MCP server

by aiballfooty

Get outcome probabilities for a match

get_match_analysis
Read-onlyIdempotent

Retrieve pre-kick-off home, draw, and away probabilities, predicted outcome, confidence, favourite, and capture time for a match; finished matches also include final score and hit status.

Instructions

For one match: the model's home / draw / away probabilities as recorded before kick-off (fractions that sum to 1), its most likely outcome, a confidence score, the pre-match favourite, and when the read was captured. For a finished match it also returns the final score and whether the most likely outcome happened (hit). This version returns the balanced model; models_available lists what is included.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoLanguage for league and team names: en (English) or ms (Malay).en
match_idYesmatch_id from list_matches.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, openWorld, non-destructive), and the description adds substantive domain behavior beyond them: probabilities are captured before kick-off, they are fractions summing to 1, and finished matches additionally yield the final score and a hit flag. This is meaningful content-level disclosure that annotations cannot supply.

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?

A single dense paragraph, front-loaded with what is returned, then the model-version caveat. Every clause conveys a distinct returned field or behavioral fact; no filler.

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?

With no output schema, the description carries the burden of describing return values and does so field-by-field, including the finished-match case. The only gap is that the other available model variants are gestured at rather than explained, which matters since only the balanced model is returned here.

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 match_id's provenance (from list_matches) and lang's en/ms enum are fully documented in the schema. The description adds nothing about parameter syntax or behavior, so the baseline of 3 applies.

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 description names the exact resource (one match), the model version returned (balanced), and enumerates the returned fields (home/draw/away probabilities, likely outcome, confidence, favourite, capture time, and for finished matches the final score and hit flag). It is clearly distinct from list_matches and get_open_record. It stops short of a crisp verb-plus-resource framing but is unambiguous about what the tool produces.

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?

'For one match' implicitly contrasts with the list_matches sibling, and the note that 'models_available lists what is included' hints at a model-selection mechanism, but there is no explicit when-to-use / when-not-to-use statement or named alternative tool for retrieving other model variants.

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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