HaterPicks API

Get arbitrage opportunities

get_arbitrage

Read-onlyIdempotent

Markets where backing every outcome at different Australian bookmakers returns more than the total stake: the profit % and each leg's side, bookmaker and decimal price, best first. Prices move fast; check them at the bookmaker before acting. Needs the Pro plan ($79 AUD a month) or higher. Counts as one API call (GET /api/v1/arbs).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_arbitrage"
  }
}

Input Schema

Name	Required	Description
`limit`	No	How many rows to return (default 20, at most 100). Use search or market to narrow first.
`sport`	No	Only this sport, e.g. AFL or SOCCER (any case).
`min_profit_pct`	No	Minimum guaranteed return, percent of total stake (default 0.5).

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 9, 2026

TDQS

A4.2/5.0

Behavior5/5

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

Beyond the readOnly/idempotent/openWorld annotations, it discloses consequential behavior: the Pro plan requirement at $79 AUD a month, that it consumes one API call against GET /api/v1/arbs, and that prices are volatile and must be verified at the bookmaker before acting. Those are exactly the operational and cost facts an agent cannot infer from structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is front-loaded with the definition before the caveats, and every clause carries information (definition, freshness warning, plan requirement, call cost). The single long sentence with semicolons is dense but not padded.

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 still tells the agent the shape of the result (profit % plus per-leg side, bookmaker, decimal price, ordered best first) and the precondition of a Pro plan. Together with the fully documented schema, an agent has enough to call it correctly and interpret the response.

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 coverage is 100%, so all three parameters (limit, sport, min_profit_pct) are already documented with defaults and ranges. The description adds no additional semantics about them, so the baseline of 3 applies.

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 names a specific resource and defines it precisely: markets where backing every outcome at different Australian bookmakers returns more than the total stake, plus the fields returned (profit %, each leg's side, bookmaker, decimal price, sorted best first). That concept is clearly distinct from sibling tools like get_ev and get_best_price without needing to name them.

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?

Usage is implied by the definition and the 'prices move fast' caveat, and the schema note suggests narrowing with search or market first. But there is no explicit when-to-use guidance relative to get_ev or get_best_price, and no statement of when this tool is the wrong choice.

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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