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meridian-edge-mcp

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get_consensus

Retrieve aggregated consensus probabilities from multiple prediction markets, including trend direction and market agreement spread. Filter by sport to get active events.

Instructions

Get real-time prediction market consensus probabilities.

Returns aggregated consensus from multiple regulated prediction markets. Each event shows the collective probability, trend direction, and how much markets agree (spread).

Args: sport: Filter by sport — NBA, NFL, MLB, NHL, MLS, POLITICS, or omit for all active events. limit: Number of events to return (1–20, default 10).

Returns: Formatted consensus data with probabilities, trends, and confidence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sportNo
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries full burden. It mentions 'real-time' data and describes output fields (probability, trend direction, spread). It does not discuss rate limits, authentication, or error handling, but for a simple read-only tool, the coverage is adequate.

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 concise (~100 words) with a clear structure: a one-line purpose, a paragraph on what is returned, and labeled sections for args and returns. Every sentence adds value without redundancy.

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

Completeness5/5

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

Given the tool has only 2 optional parameters, no nested objects, and an output schema exists, the description covers all essential aspects: purpose, parameter details, and return fields. No gaps are apparent.

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

Parameters5/5

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

The input schema has 0% description coverage, so the description adds crucial meaning: it lists explicit enum values for 'sport' (NBA, NFL, etc.) and explains the 'limit' parameter range (1–20, default 10). This goes well beyond the bare schema.

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?

The description clearly states 'Get real-time prediction market consensus probabilities', specifying both the action (get) and the resource (consensus probabilities). It distinguishes this tool from siblings like get_markets and get_opportunities by emphasizing that it returns aggregated consensus from multiple markets.

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 description explains what the tool returns but does not explicitly state when to use this tool versus alternatives (e.g., get_markets). It provides no 'when not to use' guidance or explicit comparison to siblings, sticking only to a general purpose statement.

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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