WritersLogic verifiable AI agents

market-predictor

Prediction Market Analyzer — Independent probability that a Polymarket or Kalshi market resolves Yes, checked against the live book. Pass a market (slug, URL, conditionId or Kalshi ticker) and/or news text: the agent reads the rules, end date and best asks, estimates 0-100 with live web search (falling back to model knowledge if search is slow), and returns verdict.action BUY_YES / BUY_NO / NO_TRADE with edge in bps after tak

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": "market-predictor"
  }
}

Input Schema

Name	Required	Description
`text`	No	News excerpt or event description. Required unless market is given; with market, extra evidence you want weighed.
`market`	No	Polymarket slug, market URL or conditionId, or a Kalshi ticker or URL. Reads the question, rules, end date and live best asks; marketPriceYes then defaults to the book mid. A resolved market returns its outcome without a model call.
`question`	No	Exact market question; inferred when omitted (read from the market when market is given).
`minEdgePct`	No	Minimum edge after taker fees, in percentage points, before verdict.action is BUY_YES or BUY_NO (default 5). A low-confidence estimate never produces a BUY.
`bankrollUsd`	No	Converts the quarter-Kelly fraction into verdict.size_usd.
`marketPriceYes`	No	Current implied Yes probability, used to compute edge. Percent (0-100) by default; a value strictly between 0 and 1 is read as a 0-1 market price (Polymarket/Kalshi style) unless marketPriceScale says otherwise.
`marketPriceScale`	No	Force the unit of marketPriceYes: percent (35 = 35%) or fraction (0.35 = 35%).

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 15, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations, the description carries the burden and discloses meaningful behavior: live web search with fallback to model knowledge, defaulting marketPriceYes to the book mid, shortcutting resolved markets, and computing edge after taker fees. It does not explicitly state whether any trade is ever placed or discuss auth, rate limits, or latency, leaving some operational gaps.

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 purpose is front-loaded and most clauses add useful scope, but the description is one dense sentence and ends with a truncated phrase ('after tak'), reducing polish.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex seven-parameter tool with no annotations and no output schema, the description explains inputs and process well but only partially describes the return object. It mentions verdict.action and edge, but omits other likely return fields such as probability, confidence, and size, so an agent cannot fully anticipate 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 description coverage is 100%, so the parameter meanings are already fully documented in the input schema. The description reinforces the market/text inputs and verdict output but adds little semantics beyond what the schema already provides.

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?

States a specific verb and resource: it produces an independent probability that a Polymarket or Kalshi market resolves Yes, checked against the live book, and returns a verdict action. This is clearly distinguishable from the legal, audit, and token-focused sibling tools.

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 says to pass a market and/or news text and explains the workflow of reading rules, end date, and best asks before estimating. It gives clear usage context but does not explicitly state when not to use it or name alternatives.

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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