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Bet Better Data

by io.github.edushinka
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Bets feed (licensed)

get_bets_feed
Read-onlyIdempotent

The licensed Bets feed: the model's selections that fit a proven group, sent before the start, with our chance, the minimum price that still fits, the confidence word and the group in words. graded=true returns the last 30 days graded. Needs the Bets feed.

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_bets_feed"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMost rows to return (default 50).
gradedNotrue = graded selections of the last 30 days.
leagueNoOptional league or board filter, for example nfl or epl.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already establish that this is a safe, read-only, idempotent operation. The description adds useful behavioral context beyond annotations: it states the feed is pre-start, mentions the licensing requirement, and explains the graded-mode time window.

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?

The description is short and front-loads what the feed contains, then adds the graded behavior and prerequisite. The list of returned attributes is slightly dense but each item adds meaningful information about the feed.

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?

With no output schema, the description usefully names the key returned fields: chance, minimum price, confidence word, and group. It also covers the main prerequisite and graded mode, though it does not elaborate on pagination or response structure beyond the fields mentioned.

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 are already documented. The description reinforces the graded parameter's meaning by saying it returns the last 30 days graded, but does not add detail for limit or league beyond what the schema provides.

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 states a specific resource and scope: a licensed Bets feed containing model selections that fit a proven group and are sent before the start. The word 'licensed' and the focus on proven-group selections help distinguish it from get_free_picks, though the sibling is not named explicitly.

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?

The phrase 'Needs the Bets feed' gives an important prerequisite, and 'graded=true returns the last 30 days graded' clarifies one mode. However, it does not explicitly say when to choose this over get_free_picks or other prediction tools, so usage guidance remains implied rather than direct.

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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