FormFav Horse & Greyhound Racing

Get win and place predictions

get_predictions

Read-onlyIdempotent

Get ML win/place probability predictions for a race (Pro tier).

Returns probability scores for each runner: winProb, placeProb, modelRank,
and confidence level. Probabilities sum to 1.0 across all runners in the race.
Not all races have predictions — only those with sufficient runner data.

Args:
    date: Race date in YYYY-MM-DD format. Range: 30 days ago to 7 days ahead.
    track: Track/venue name (e.g. 'flemington', 'randwick').
    race: Race number (1-20).
    race_code: Race type — 'gallops' (default), 'harness', or 'greyhounds'.
    country: Country code — 'au' (default).
    timezone: IANA timezone scoping `date` (default Australia/Sydney). Must
        match what you passed to get_race_card()/get_race_results() for the
        same race, or this will not resolve to the same meeting.

Input Schema

TableJSON Schema

Name	Required	Default
`date`	Yes
`race`	Yes
`track`	Yes
`country`	No	au
`timezone`	No
`race_code`	No	gallops

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 18, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the bar is lower, and the description still adds meaningful behavior: entitlement requirement (Pro tier), partial data availability, and the normalization rule that probabilities sum to 1.0 across runners. It does not cover latency, caching, or error modes, keeping it out of the top band.

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?

Front-loads the purpose and return shape, then the availability caveat, then a clean per-parameter block. No sentence is filler, and the one operational trap (timezone mismatch silently resolving to a different meeting) is called out where the parameter is described.

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?

An output schema exists, yet the description still names the key return fields (winProb, placeProb, modelRank, confidence), and it covers everything else an agent needs: entitlement, parameter formats, availability limits, and the timezone alignment requirement with sibling calls.

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

Parameters5/5

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

Schema coverage is 0%, so the description carries the full parameter burden and does so thoroughly: date format with a range (30 days back to 7 ahead), track naming convention with examples, race number bounds (1-20), enumerated race_code values, country default, and an explicit cross-tool consistency rule for timezone.

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

Purpose5/5

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

States a specific verb and resource ('Get ML win/place probability predictions for a race') and immediately scopes it with a tier qualifier. This is clearly distinguishable from siblings like get_race_card (entries) and get_race_results (outcomes), since it returns modeled probabilities rather than factual race data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives real usage context: Pro tier gating, the availability caveat ('Not all races have predictions — only those with sufficient runner data'), and the requirement that timezone match what was passed to get_race_card()/get_race_results(). It stops short of explicitly stating when to prefer this tool over those siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All FormFav Horse & Greyhound Racing tools Inspect

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