infersports

Find Match

find_match

Read-only

Find a football/basketball fixture by natural-language name.

Args:
    query: e.g. "Man City vs Arsenal" or a single team name.
    sport: optional filter — "football" or "basketball".
    date: optional UTC date "YYYY-MM-DD" to disambiguate same-name fixtures.

Returns the best-matching event (with id, teams, league, kickoff, live score, the live match
``clock`` e.g. "2h 47" or "ht", and a confidence score) plus alternatives. Use the returned
``event_id`` with get_match_odds / compare_lines.
A ``decision`` block tells you whether it's ``safe_to_proceed`` and the suggested
``next_action`` (or ``ask_user`` when ambiguous).

Input Schema

TableJSON Schema

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

TDQS

A4.6/5.0

Behavior4/5

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

Description adds behavioral details beyond readOnlyHint annotation: returns best-matching event with live score, clock, confidence, alternatives, and decision block. No contradiction with annotations.

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?

Concise and well-structured: starts with purpose, then Args, then Returns. Every sentence adds value. No extraneous text.

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?

Comprehensive given no output schema: explains all return fields (id, teams, league, kickoff, live score, clock, confidence, alternatives, decision block). Ties usage to sibling tools. Parameter count is low, so description covers all needed context.

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 coverage, description fully compensates by explaining each parameter with examples (e.g., query: 'Man City vs Arsenal', date: 'YYYY-MM-DD', sport: 'football' or 'basketball'). Meaning is clear and actionable.

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?

Description clearly states 'Find a football/basketball fixture' with specific verb 'Find' and resource 'fixture'. It distinguishes from siblings by explaining how to use the returned event_id with get_match_odds and compare_lines, which are listed as sibling tools.

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?

Provides clear usage context: use returned event_id with specific sibling tools, and notes the decision block indicating safe_to_proceed and next_action. Lacks explicit 'when not to use' but effectively guides the agent.

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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