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infersports

by dev.infersports
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Compare Lines

compare_lines
Read-only

Compare one market across all bookmakers for a match.

Returns each book's quote line-by-line, the consensus line, the best price per outcome, each
book's overround, and de-vigged fair odds from the sharpest (lowest-margin) book.

Args:
    event_id: event id (e.g. "evt_…").
    market_type: "1x2", "asian_handicap" (default) or "totals".
    period: "full_time" (default) or "half_time".
    format: display odds format (analytics are computed in decimal regardless).
    verbosity: "full" (default) or "terse". "terse" drops the per-book ``books`` array (the bulk
        of the payload) and returns only the worked answer (best_prices / consensus_line /
        fair_odds / summary).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNodecimal
periodNofull_time
event_idYes
verbosityNofull
market_typeNoasian_handicap

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

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 discloses useful behavior: verbosity modes, what 'terse' drops (the per-book books array), and that analytics are computed in decimal regardless of display format. It also clarifies the source of fair odds ('from the sharpest (lowest-margin) book'), which is meaningful behavioral detail.

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: a one-sentence purpose followed by a scannable Args list. Every sentence adds value, and the most important information about the return payload and verbosity trade-off is included without fluff.

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?

Despite no output schema, the description states the main return components and parameter effects. It covers all five parameters, gives defaults context, and explains how the 'terse' mode changes the response. An agent has enough information to call the tool correctly and interpret the result.

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 documenting every parameter: event_id with an example, market_type with allowed values, period options, format semantics, and verbosity behavior including payload impact. This is exactly what the schema itself lacks.

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: 'Compare one market across all bookmakers for a match.' It then enumerates concrete outputs such as consensus line, best price per outcome, overround, and de-vigged fair odds, making it clearly distinct from sibling tools like compare_prob or get_match_odds.

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 makes the intended scenario clear: use this tool when you need a cross-bookmaker comparison of a single market for a given match. It does not explicitly name alternatives or state when not to use it, but the purpose is specific enough to guide selection among similar siblings.

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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