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Prediction Markets Quant

Explain This Ladder — why The Ladder reads one NFL market the way it does

explain_ladder
Read-only

Why The Ladder reads one NFL strike ladder (a game spread, a season win total or a player prop) the way it does: the read in words, where Kalshi's ladder centres beside our projection, the widest published gap, and each strike with Kalshi's price and our number (30–70% only). Use for "explain the Chiefs win total ladder", "why does the ladder lean Browns".

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": "explain_ladder"
  }
}

Input Schema

NameRequiredDescriptionDefault
statNoProp stat for a player, e.g. "receptions" or "rec_yds". Defaults to his widest ladder.
teamNoTeam code or nickname (KC, Chiefs) — its game spread ladder, or its season-win ladder with family "season_wins".
familyNoLadder family. Omit to take the team's spread first. One of: spread · season_wins · props.
playerNoPlayer name, full or partial — his prop ladder.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • removedInput schema / $schema
      Removed value: -"https://json-schema.org/draft/2020-12/schema"
  2. Added

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description goes beyond that by disclosing what the response contains — the written read, Kalshi's ladder centre vs. our projection, the widest published gap, per-strike prices — and the notable 30–70% strike filtering constraint, which materially shapes expectations.

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?

Purpose and output contents are front-loaded in the first sentence, with usage examples trailing. The first sentence is dense with clauses but every one carries information; no filler.

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?

No output schema exists, so the description must describe return values, and it does so enumerably (read text, centre vs. projection, widest gap, per-strike prices, 30–70% filter). With all four optional parameters fully documented in the schema, the agent has enough to call it correctly.

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 baseline is 3. The description adds only light semantics beyond the schema — that omitting family yields the spread ladder first, and that the output is restricted to 30–70% strikes — but does not explain interactions such as stat defaulting to the player's widest ladder or precedence between team/family/player.

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?

States a specific verb and resource — explaining how The Ladder reads a single NFL strike ladder — and enumerates the covered market types (game spread, season win total, player prop). It is clearly distinct from generic siblings like explain_game or explain_player, though it never explicitly contrasts itself with nfl_ladder or explain_prop, which cover adjacent ground.

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?

"Use for 'explain the Chiefs win total ladder'" gives concrete invocation triggers and shows how a caller phrases a request. There are no explicit exclusions or named alternatives (e.g. when to prefer explain_prop instead), which keeps it short of a 5.

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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