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

Schema Changelog

Changes observed during successful MCP inspections.

First observedJun 7, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations only declare readOnlyHint=true, so the description carries the burden of behavioral disclosure. It thoroughly explains output structure (live score, match clock, status, kickoff time, favored team with probability, summary), the 'ambiguous' status with ask_user for ambiguous queries, and best-effort favorite null behavior. This goes well beyond the 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?

The description is front-loaded with the primary purpose in the first sentence, then provides examples, detailed parameter explanations, and edge-case notes. Every sentence adds value—no fluff. The structure (purpose → usage → args → caveats) is logical and appropriately sized for a tool with 4 parameters and no output schema.

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?

With no output schema, the description must explain return values, and it does: live score, match clock, status, kickoff time, favored team with probability, summary, and the ambiguity behavior. It covers all parameters, defaults, and error/edge cases. For a 1-required-param tool, nothing an agent needs to call it correctly is missing.

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 description coverage is 0%, so the description must fully explain each parameter. It does: query (natural-language fixture/team with examples), timezone (IANA names, default UTC), sport (football/basketball), and date (to disambiguate). This adds meaning beyond the bare schema fields, making parameter usage clear.

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 opens with a specific verb and resource: 'Get the basics for a match in ONE call: the score, whether it's live, when it kicks off, and who's favored.' It enumerates concrete outputs and sample user questions, making the tool's purpose unmistakable and distinct from siblings like get_match_odds or find_match which serve different needs.

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 clear context for when to use the tool ('No betting knowledge needed', answers simple questions like 'who's winning?') and explains parameter usage that influences selection (e.g., timezone for local time, sport filter). However, it does not explicitly name alternative tools or state 'use this instead of X' for specific scenarios, so it falls short of the top score.

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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