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DF Market Intelligence

DF Market Intelligence

by nasosal92
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

predictions_movers

Identify prediction markets whose implied probability shifted the most in the last 24 hours, filtered by minimum volume, to spot where the crowd changed its mind.

Instructions

Prediction market movers — Markets whose implied probability moved the most in the last 24h — what the crowd suddenly changed its mind about. Filtered by minimum volume. Costs $0.01 USDC per call (x402 on Base), paid automatically from your configured wallet.

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": "predictions_movers"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo1-100, default 25
min_volumeNoMinimum 24h volume in USD, default 10000

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.9/5.0
Behavior4/5

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

No annotations exist, so the description carries the full load, and it does disclose the important behavioral trait: a paid call at $0.01 USDC per invocation via x402 on Base, auto-deducted from the configured wallet. That cost/payment disclosure is exactly the kind of context an agent needs before calling. It omits response shape, pagination, and error/insufficient-balance behavior, keeping it short of a 5.

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 scope are front-loaded, and the cost sentence is a separate, earned line given the payment model. The framing clause is slightly decorative but short enough not to hurt.

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?

For a two-optional-parameter list tool with no output schema and no annotations, the description covers what it returns conceptually (markets with largest probability moves), the filter dimension, and the payment model. Missing only return format and pagination, which are minor here.

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 both parameters (limit, min_volume) are already fully documented with ranges and defaults. The description's 'filtered by minimum volume' merely restates min_volume, adding no syntax or semantics beyond the schema. Baseline 3 is appropriate.

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 metric — the markets whose implied probability moved the most over the last 24h — which is a distinct concept from the sibling predictions_top and predictions_search. The verb/resource framing plus the plain-language gloss ('what the crowd suddenly changed its mind about') makes it unambiguous.

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 24h-mover scope and the min_volume filter imply when this tool is useful relative to a generic top-markets list, but no sibling is named and no exclusion is stated. The agent must infer that predictions_top is the alternative for volume-ranked rather than movement-ranked data.

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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