infersports

Match Info

match_info

Read-only

Get the basics for a match in ONE call: the score, whether it's live, when it kicks off, and who's favored.

No betting knowledge needed — this answers "who's winning?", "what's the score?", "what time does
Brazil play (in my timezone)?", "who's the favorite?". Returns the live score + match clock, the
status, the kickoff time (in ``timezone`` if you pass an IANA name like "America/New_York"), the
favored team with a plain win probability (de-vigged from the 1x2 line), and a ready-to-read
``summary`` you can quote directly.

Args:
    query: natural-language fixture or team, e.g. "Brazil vs Argentina" or just "Brazil".
    timezone: optional IANA timezone (e.g. "America/New_York", "Asia/Shanghai") for the kickoff
        time; default UTC.
    sport: optional filter — "football" or "basketball".
    date: optional UTC date "YYYY-MM-DD" to disambiguate same-name fixtures.

On an ambiguous query, ``status`` is "ambiguous" and ``ask_user`` carries a prompt — do not guess.
``favorite`` is best-effort (null when no 1x2 is on file for the fixture).

Input Schema

TableJSON Schema

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

TDQS

A4.8/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, the description details the output components, behavior on ambiguity (status 'ambiguous', ask_user prompt), and edge cases (favorite is best-effort). This fully discloses the tool's behavior.

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?

The description is well-structured with a clear purpose first, then parameter explanations, then edge cases. Every sentence adds value without redundancy. It is appropriately detailed for the tool's complexity.

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 fully explains the return values and behaviors. It covers all parameters, handles ambiguity, and is self-contained. Sibling context is provided via the sibling list, but the description itself is complete.

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, the description thoroughly explains each parameter: query as natural-language, timezone as IANA, sport filter, date format. It includes examples and usage notes, adding essential meaning beyond the schema.

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 the verb 'Get' and the resource 'basics for a match', listing specific outputs (score, live status, kickoff, favorite). It distinguishes itself from siblings like get_match_odds by emphasizing a single call for basic info.

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 provides implicit usage context (e.g., 'No betting knowledge needed', handling ambiguous queries with ask_user) but does not explicitly compare to sibling tools or state when to avoid. The guidance for ambiguous queries is helpful.

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.

Resources

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