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BotOrNot

get_prediction_markets

Read-only

Prediction-market venues (Kalshi, Polymarket US, Gemini Predictions) with their CFTC status and official sources, plus the documented prediction-market bots and their report cards.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety and closed-world profile is covered. The description adds useful content scope (what categories of data come back) but says nothing about whether this is a fixed reference snapshot versus live data, or how the embedded bots/report cards relate to the dedicated sibling tools.

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?

A single dense sentence that front-loads the primary resource (venues) before the secondary payload (bots and report cards). It is appropriately sized for a no-argument reference tool, though the trailing 'plus the documented...' clause is slightly tacked on.

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 parameters and no output schema, the description carries the burden of explaining return content, and it does so by listing the main categories (venues, CFTC status, sources, bots, report cards). It stops short of clarifying the relationship between the embedded bot/report-card data and the dedicated bot tools.

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

Parameters4/5

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

The tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline of 4 applies. No parameter semantics are missing because none exist.

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 names the concrete resource (prediction-market venues — Kalshi, Polymarket US, Gemini Predictions) and enumerates the payload (CFTC status, official sources, documented bots, report cards). It is specific enough to distinguish the content, but it does not explicitly contrast the tool with siblings like list_bots or get_report_card, whose output overlaps with the 'bots and report cards' portion.

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

Usage Guidelines2/5

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

There is no when-to-use statement, no prerequisites, and no exclusions despite the obvious overlap with list_bots, get_bot, and get_report_card. The agent must infer that this is a static reference lookup rather than a query tool.

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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