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MCP Predictive Market

MCP Predictive Market

by EricGrill
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

search_markets

Search prediction markets across platforms to find specific questions, compare odds, and identify arbitrage opportunities.

Instructions

Search for prediction markets across platforms

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": {
      "query": "<query>"
    },
    "name": "search_markets"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesSearch query (e.g., 'Will Trump win 2024?')
platformsNoOptional: filter to specific platforms

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?

No annotations are provided, so the description carries full burden for behavioral disclosure. It states the action ('search') but doesn't cover critical traits like whether this is a read-only operation, if it requires authentication, rate limits, pagination, or what the search scope entails (e.g., real-time vs. historical). This is a significant gap for a tool with no 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, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it highly concise and well-structured for quick understanding.

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 complexity of a search tool with no annotations and no output schema, the description is incomplete. It doesn't explain return values, error handling, or behavioral nuances, leaving the agent under-informed about how to interpret results or handle edge cases.

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 schema already documents both parameters ('query' and 'platforms') with descriptions. The description adds no additional meaning beyond implying a cross-platform search, which is redundant with the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

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 verb ('search') and resource ('prediction markets across platforms'), providing a specific purpose. However, it doesn't explicitly differentiate from sibling tools like 'browse_category' or 'get_tracked_markets', which might also involve market discovery, so it lacks sibling differentiation for a perfect score.

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, exclusions, or compare to siblings like 'browse_category' for categorical browsing or 'get_tracked_markets' for retrieving saved markets, leaving the agent without usage context.

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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