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AI Ball MCP server

AI Ball MCP server

by aiballfooty
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

Get the open record

get_open_record
Read-onlyIdempotent

Retrieve the public record of how pre-kick-off football prediction reads performed, either overall or for one Monday-to-Sunday week, including hit counts, hit rates, baselines, and confidence bands.

Instructions

How the model's pre-kick-off reads turned out. Without arguments: the whole public record. With week_start: one Monday-to-Sunday week. Returns matches counted (n), how many went the model's way (hits), the same matches scored by always taking the pre-match favourite and by one in three, and the breakdown by confidence band. hit_rate is a percentage; null means the sample is under min_band_sample, so quote the counts instead. Misses are counted the same way as hits.

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_open_record"
  }
}

Input Schema

NameRequiredDescriptionDefault
tzNoIANA time zone that decides which calendar day a match belongs to.Asia/Kuala_Lumpur
week_startNoMonday of the week, YYYY-MM-DD. Omit for the whole record.

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, non-destructive, openWorld), so the description is free to spend its words on behavior the annotations cannot express. It does so usefully: null hit_rate signals a sample under min_band_sample, the agent should quote counts instead, and misses are counted identically to hits. That is real interpretive context beyond structured fields.

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?

Four dense sentences, front-loaded with what the record is before moving to argument behavior and output interpretation. Every sentence carries information, though the output-semantics tail is slightly packed and could be split for scanability.

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 must carry return-value meaning and largely does: it names the fields, explains hit_rate units, the null case, and how the benchmark comparisons are constructed. The remaining gap is the absence of any sibling routing or an explicit note on what 'public record' scope excludes.

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 description coverage is 100%, so the baseline is 3 and the schema already explains tz and week_start. The description still adds value by stating the universe each branch returns ('the whole public record' vs 'one Monday-to-Sunday week') and by defining the null/hit_rate contract, which no schema field captures.

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 states concretely what the tool retrieves: the model's pre-kick-off prediction record, whole or sliced by week, with an enumerated return set (n, hits, hit_rate, confidence-band breakdown). An agent can tell this is a historical performance read rather than a match lookup. It does not, however, differentiate itself from list_matches or get_match_analysis, which is the only thing keeping it from 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?

Usage is only implied through the parameter branches ('Without arguments: the whole public record. With week_start: one Monday-to-Sunday week'), which tells the agent how the call behaves but not when to pick it over its siblings. There is no explicit when-to-use or when-not-to-use guidance relative to list_matches or get_match_analysis.

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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