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demwick

Polymarket Agent Mcp

traders.score

Calculate a conviction score (0-100) for traders based on win rate, volume, consistency, experience, and diversity to identify reliable traders for copy trading.

Instructions

Calculate a conviction score (0-100) for a trader across 5 dimensions: win rate, volume, consistency, experience, and diversity. Higher scores indicate more reliable traders for copy trading. Pro feature.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
addressYesTrader's Ethereum wallet address (0x...) to score
Behavior2/5

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

With no annotations provided, the description carries full burden. It discloses the output range (0-100) and scoring dimensions, but lacks critical behavioral details: whether this is a read-only operation, if it requires authentication, rate limits, computational cost, or how the score is calculated from the dimensions. For a scoring tool with zero annotation coverage, this is insufficient.

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 efficiently structured in two sentences: the first explains the core functionality and scoring dimensions, the second adds context about reliability and pro feature. Every phrase earns its place with no redundant information.

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

Completeness3/5

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

Given no annotations and no output schema, the description partially compensates by explaining the scoring dimensions and output range. However, for a tool that performs analysis (likely read-only but computationally intensive), it should more fully describe behavioral aspects like execution characteristics, error conditions, or output format to be complete.

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 input schema has 100% description coverage, clearly documenting the single 'address' parameter. The description adds value by contextualizing it as 'Trader's Ethereum wallet address' and linking it to the scoring purpose, though it doesn't provide additional syntax or format details beyond the schema's pattern.

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 the specific action ('Calculate a conviction score'), the target resource ('for a trader'), and the scoring dimensions (win rate, volume, consistency, experience, diversity). It distinguishes from sibling tools like 'traders.analyze' or 'traders.backtest' by focusing specifically on scoring rather than general analysis or historical testing.

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 implies usage context by mentioning 'copy trading' and 'Pro feature', suggesting it's for evaluating traders to copy. However, it doesn't explicitly state when to use this vs. alternatives like 'traders.analyze' or 'traders.discover', nor does it provide exclusions or prerequisites beyond the pro feature note.

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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