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Sports closing prices and lines

sports_closing_lines
Read-onlyIdempotent

Each bookmaker's last head-to-head price, handicap line and total before the start of every match in a range (default the last 7 days, at most 92), with the consensus close. For CLV and model training. Costs 5 credits. Plan: Standard and above.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "sport_key": "<sport_key>"
    },
    "name": "sports_closing_lines"
  }
}

Input Schema

NameRequiredDescriptionDefault
to_dateNoMatches starting on or before this. Default now.
from_dateNoMatches starting on or after this. Default 7 days ago.
sport_keyYesSport key from sports_list, e.g. afl, nrl, nba or soccer_epl. An unknown key is answered with the valid ones.
bookmakersNoOnly these bookmaker keys, e.g. ["tab", "sportsbet", "ladbrokes_au"]. Default: every book. An unknown key is refused free of charge with the list of valid keys.

Output Schema

NameRequiredDescriptionDefault
dataYesThe API response, with links and empty fields removed.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive behavior. The description adds important non-annotation context: 5-credit cost, Standard-and-above plan requirement, default 7-day window, and 92-day maximum range.

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 compact and front-loaded with the main resource and return details. Cost and plan constraints are presented as short, skimmable sentences with no wasted text.

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?

Given an output schema and rich annotations, the description is nearly complete for invocation: it covers the data returned, date-range defaults, cost, and plan eligibility. The main missing context is how this tool relates to sibling closing_lines and other odds tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all four parameters, including defaults and constraints. The description restates the date-range defaults and cap but adds no new parameter meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the resource: each bookmaker's closing head-to-head price, handicap line, total, and consensus close for matches in a date range. It is specific about what is returned, but it does not explicitly differentiate this from the sibling closing_lines tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a use case (CLV and model training) and cost/plan constraints, which implies when to use it. However, it does not compare against obvious alternatives such as closing_lines, sports_odds, sports_best_odds, or evaluate_clv, leaving the agent to infer selection.

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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