Get the prediction market leaderboard — top wallets by prediction score.
Returns wallets ranked by their composite prediction score,
which factors in win rate, ROI, volume, sample size, and recency.
Args:
limit: Number of entries to return (default 20, max 100)
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It explains the ranking factors (win rate, ROI, volume, sample size, recency) and the limit parameter behavior (default 20, max 100), which is useful. However, it does not disclose whether the data is live, cached, paginated, or whether it requires authentication, leaving some behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: the first sentence states the core purpose, the second explains the ranking criteria, and the Args section is minimal and directly tied to the schema. Every sentence earns its place with no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with one optional parameter and an output schema, the description is nearly complete. It covers what the tool returns (ranked wallets by composite score), the ranking factors, and the parameter semantics. The only missing context is whether the leaderboard is global or scoped, and any rate-limit or freshness caveats, but these are minor given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It does: it explains the 'limit' parameter's purpose, default value, and maximum. This adds meaning beyond the raw schema, which only shows a default of 20. The only minor gap is not explaining what happens if limit is exceeded (e.g., clamped or error).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Get the prediction market leaderboard — top wallets by prediction score.' It specifies the resource (prediction market leaderboard), the action (get), and the ranking criterion (composite prediction score). It also distinguishes itself from sibling tools like mt_prediction_link and mt_prediction_wallet by focusing on the leaderboard rather than linking or wallet details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by explaining what the leaderboard ranks and the optional limit parameter, but it does not explicitly state when to use this tool versus alternatives or when not to use it. It provides no exclusions or alternative routing, so an agent must infer that this is the go-to for leaderboard 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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