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Glama

trading_leaderboard

Get the public trading leaderboard — top paper trading bots ranked by P&L, win rate, and win streak. Shows bot name, strategy type (mean_reversion/momentum/breakout/etc.), chain, trade count, win rate, P&L, and win streak. Only strategies where the user opted in appear. Use to benchmark your own strategy or explore what methods are winning. CALL FORMAT: trading_leaderboard({})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (default 20, max 50).

TDQS

A4.3/5.0
Behavior4/5

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

No annotations provided, so description carries full burden. It clearly indicates it is a read operation ('get'), explains data limitations (opted-in only), and provides call format. No side effects mentioned, but sufficient for the tool's purpose.

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?

Four sentences, front-loaded with purpose, no redundant information. Every sentence adds value (purpose, fields, limitations, usage).

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?

For a simple leaderboard tool with one optional parameter, the description fully explains what is returned, what data appears, and how to use it. No output schema needed; output is clearly described.

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?

Single parameter 'limit' is fully described in schema (default 20, max 50). Description does not add additional meaning beyond the schema, achieving baseline for high schema coverage.

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 it retrieves the public trading leaderboard, listing specific fields (bot name, strategy type, P&L, etc.). It is distinct from sibling tools like trading_stats or trading_journal.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states 'Use to benchmark your own strategy or explore what methods are winning,' and notes that only opted-in strategies appear. Implicit guidance on when to use, but no explicit alternatives or when not to use.

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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TDQS

A3.5/5.0
Disambiguation3/5

Many tools have distinct purposes, but there are several overlapping or redundant tools (e.g., leadsignal vs leadsignal_generate, multiple code audit tools, multiple trading proposal/journal tools, and several 'universal' entry points like zambo_help, zambo_ask, zambo_universal). Descriptions help, but the volume creates ambiguity.

Naming Consistency3/5

Naming conventions vary across prefixes (zambo_, zambot_, axis_, presence_, trading_, etc.), with some tools using single words (weather, translate) and others using verb_noun patterns. Aliases like leadsignal_generate for leadsignal break consistency. While prefixes provide some grouping, the overall pattern is mixed.

Tool Count2/5

125 tools is excessive for a single MCP server, even if the server aims to be a universal stack. This makes it overwhelming for agents to navigate and increases the likelihood of misselection. Many tools could be split into domain-specific servers.

Completeness5/5

The tool surface is extraordinarily comprehensive, covering agent identity, cross-layer orchestration, code analysis, content generation, legal scanning, lead generation, trading, on-chain data, and more. Nearly any common agent task is supported with multiple tools, leaving few obvious gaps.

Resources