infersports

Get Opening Line

get_opening_line

Read-only

Get the opening odds (初盘) for a fixture, paired with the current price, in ONE call.

Resolves the fixture, then for each book returns its **true opening** (first-seen) quote alongside
the current quote — so you can read movement directly. For 1x2 that's a price move; for totals/AH
compare ``line`` (the opening line) vs ``current_line`` to read the line move (the sharp book often
opens days earlier). A market with no opening yet on file is omitted.

Args:
    query: natural-language fixture, e.g. "Real Madrid vs Barcelona" or a single team.
    markets: optional filter — any of "1x2", "asian_handicap", "totals".
    bookmakers: optional filter — bookmaker keys, e.g. ["pinnacle", "crown"].
    period: optional — "full_time" or "half_time" (default: both).
    format: odds format — decimal | hk | malay | american | indonesian | probability.
    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 disambiguation
prompt — do not assume a match. Best-effort: a book/line with no opening on file is omitted.

Input Schema

TableJSON Schema

Name	Required	Default
`date`	No
`query`	Yes
`sport`	No
`format`	No	decimal
`period`	No
`markets`	No
`bookmakers`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedJun 7, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses critical behaviors: ambiguous queries return status 'ambiguous' with an ask_user prompt, and books/lines with no opening are omitted on a best-effort basis. It also explains the line vs current_line semantics for totals/AH, which is essential for correct interpretation. No contradictions 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?

The description is front-loaded with a concise purpose statement, then structured with a clear Args list and additional notes on ambiguity and best-effort behavior. Every sentence adds value; there is no fluff or redundancy. The length is justified by the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers all parameters and explains key return behaviors (status, ask_user, line vs current_line, omission), but since there is no output schema, it does not fully specify the response structure. However, it provides enough for an agent to know what to expect and how to handle ambiguous results, making it nearly complete for a tool of this complexity.

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 every parameter in the Args section, including examples (query: 'Real Madrid vs Barcelona'), allowed values (markets: '1x2', 'asian_handicap', 'totals'), and format options. This adds substantial meaning beyond the raw 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 states a specific verb (Get) and resource (opening odds for a fixture) paired with the current price, and explicitly distinguishes its purpose from siblings like get_match_odds (current odds) and get_sharp_line. It also explains how to read movement, making the tool's unique value immediately clear.

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 gives clear context on when to use this tool—when you need to compare opening vs current odds to read movement—and explains the interpretation for different market types. However, it does not explicitly name alternative tools or state when not to use it, relying on the reader to infer from the context.

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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