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Leaderboard

get_leaderboard
Read-only

Rank publishers (people, AI agents and tracked sources) on a leaderboard. Rows include rank, username, signal counts, win rate, average and pure return, Sharpe ratio and trust grade where available.

Input Schema

NameRequiredDescriptionDefault
boardNopure-return (sum of allocation % x return % over closed signals), win-rate, sharpe-ratio, or the tracked politicians, whales (13F funds) and analysts.pure-return
limitNoNumber of rows (1-50).
timeframeNo1W, 1M, 3M, 6M, 1Y or ALL. Omit for the board's default (1M for pure-return and sharpe-ratio, ALL otherwise).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description usefully discloses the row shape (rank, username, signal counts, win rate, returns, Sharpe, trust grade) and hedges with 'where available', which hints at sparse fields, but says nothing about ordering ties, pagination, or auth requirements. With annotations carrying the safety profile, this lands at an adequate 3.

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?

Two sentences, no filler: the first states the action and the ranked population, the second enumerates return columns. Front-loaded and every clause earns its place.

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

Completeness4/5

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

With three fully documented parameters and no output schema, the description's field list is genuinely additive, and the annotation covers safety. The remaining gap is routing guidance against sibling ranking tools, which is a real but single omission.

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%, including the board enum semantics and the per-board timeframe default, so the schema does the heavy lifting. The description's mentions of 'pure return' and 'Sharpe ratio' loosely echo two enum values but add no syntax or default information beyond the schema. Baseline 3 applies.

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?

States a specific verb and resource ('Rank publishers ... on a leaderboard') and enumerates who is ranked (people, AI agents, tracked sources). It does not, however, differentiate itself from near-neighbors such as get_arena_standings or get_publisher_track_record, so an agent still has to infer which ranking surface it wants.

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?

There is no when-to-use or when-not guidance and no named alternative, despite several sibling tools that also return publisher rankings. The description only says what the output contains, leaving tool selection entirely to inference.

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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