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MCP Predictive Market

MCP Predictive Market

by EricGrill
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

track_market

Add prediction markets from major platforms to your tracking watchlist for monitoring odds and detecting arbitrage opportunities.

Instructions

Add a market to your tracking watchlist

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "platform": "<platform>",
      "market_id": "<market_id>"
    },
    "name": "track_market"
  }
}

Input Schema

NameRequiredDescriptionDefault
platformYesPlatform name (manifold, polymarket, etc.)
market_idYesThe market's native ID
aliasNoOptional friendly name for the market

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a write operation ('Add'), but doesn't specify if this requires authentication, has side effects (e.g., duplicates), rate limits, or what happens on success/failure. This is inadequate for a mutation tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, direct sentence with zero wasted words. It's appropriately sized and front-loaded, clearly stating the tool's core function without unnecessary elaboration.

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

Completeness2/5

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

Given the tool's complexity (a mutation with 3 parameters) and lack of annotations or output schema, the description is incomplete. It doesn't explain behavioral aspects like permissions, error handling, or return values, leaving significant gaps for an agent to use it effectively.

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?

The schema description coverage is 100%, so the schema already documents all parameters (platform, market_id, alias). The description adds no additional meaning beyond implying these parameters are used to identify and optionally name a market for tracking, which is minimal value over the schema.

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 clearly states the action ('Add') and resource ('a market to your tracking watchlist'), making the purpose understandable. However, it doesn't differentiate this tool from sibling tools like 'get_tracked_markets' or 'search_markets' beyond the basic verb, leaving some ambiguity about its specific role in the toolset.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a watchlist), exclusions, or comparisons to siblings like 'get_tracked_markets' (for viewing) or 'search_markets' (for finding), leaving the agent to infer usage from context alone.

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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