infersports

Score Probabilities (market-implied)

score_prob

Read-only

Market-implied correct-score probabilities for ONE football match — ONE call.

Inverts the de-vigged 1x2 (power de-vig) + totals prices into a bivariate-Poisson
(Dixon-Coles) score grid and returns the top-K scorelines with probabilities, the
fitted goal rates (lambda_home/lambda_away), and how much probability mass the list
covers. The asian-handicap fair price is held out of the fit and reported as
``ah_ev_check`` (≈0 means the three markets are internally consistent). This is the
market's own distribution, never an InferSports prediction. Football only, one match
per call.

Args:
    query: natural-language fixture, e.g. "Mexico vs South Africa" or a single team.
    sport: optional filter; only "football" is supported by this tool.
    date: optional UTC date "YYYY-MM-DD" to disambiguate same-name fixtures.
    top: how many scorelines to return (default 5, max 10).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`top`	No
`date`	No
`query`	Yes
`sport`	No

TDQS

A4.5/5.0

Behavior4/5

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

Annotations declare readOnlyHint=true, which is consistent. The description adds substantial behavioral detail: the statistical model (bivariate-Poisson, Dixon-Coles), de-vigging process, and the fact it's market-implied not a prediction. It also notes one-call-only behavior.

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?

The description is front-loaded with purpose and structured with a summary paragraph then arg list. Though it is somewhat verbose, every sentence adds value and there is no redundancy. A tighter second sentence could be made, but overall well-organized.

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?

Given no output schema, the description thoroughly explains the return: top-K scorelines, probabilities, goal rates, and the ah_ev_check verification. It covers the football-only constraint, single match scope, and the model used. This is sufficient for an agent to invoke correctly.

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?

With 0% schema description coverage, the description fully compensates by explaining each parameter: query (natural-language fixture with examples), sport (only football), date (UTC disambiguation), and top (scoreline count with defaults). This adds critical meaning beyond the schema types.

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?

The description clearly states it computes 'market-implied correct-score probabilities for ONE football match' with a specific verb (inverts) and resource (score grid). It distinguishes from siblings by being exclusively about score probabilities and not comparison or line finding.

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?

The description plainly states 'Football only, one match per call' and explains optional params for disambiguation. However, it does not explicitly tell when to avoid this tool or compare with alternatives like compare_prob.

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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TDQS

A4.6/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: compare_lines contrasts bookmakers, compare_prob evaluates external probabilities, find_arbitrage detects arbitrage opportunities, find_match resolves fixtures, etc. Even related tools like get_sharp_line (one-call line) and compare_lines (event_id-based comparison) are complementary rather than overlapping. No ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., compare_lines, find_match, list_bookmakers). Verbs like compare, find, get, list, scan, score are uniform. No mixing of conventions or vague names.

Tool Count5/5

With 17 tools covering fixture discovery, odds retrieval, comparisons, value/arb detection, line movement, results, and slate scanning, the count is well-scoped for a sports betting odds API. No unnecessary tools, and the set feels complete without being bloated.

Completeness5/5

The tool surface covers the full lifecycle: find matches (find_match, list_events, list_today_matches), get odds (get_match_odds, get_sharp_line, compare_lines), detect value/arb (find_value, find_arbitrage, scan_slate), analyze lines (get_opening_line, explain_handicap, score_prob), and retrieve results (get_result, list_results). No obvious gaps for the stated read-only odds analysis domain.

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