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trading_stats

Get your complete trading performance dashboard: total P&L, win rate, trade count, today's P&L, live vs paper breakdown. Use to check progress toward live trading unlock (requires 20 paper trades). CALL FORMAT: trading_stats({strategy_id: 'strat_abc123'}) or trading_stats({email: 'you@example.com'}) for all strategies.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailNoYour email — used to identify your trading history.
strategy_idNoFilter stats to a specific strategy. Omit to get aggregate across all strategies.

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries full burden. It lists the return fields (P&L, win rate, etc.) and implies a read-only query via 'Get', but does not explicitly state it is non-destructive or provide any side effects, which is a minor gap.

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?

Extremely concise: two sentences plus a call format line. Front-loaded with key outputs and use case. No redundant information.

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?

Given no output schema, the description provides a good list of return metrics and a prerequisite (20 paper trades). It could be more detailed on format or computation, but is sufficient for a simple stats tool.

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?

Schema coverage is 100% with each parameter already described. The description adds value by showing example call formats and explaining that omitting strategy_id gives aggregate stats, which reinforces usage beyond the 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 it returns a trading performance dashboard with specific metrics (P&L, win rate, etc.) and a specific use case (check progress toward live trading unlock). The verb 'Get' and resource 'complete trading performance dashboard' are specific and distinguish it from siblings like leaderboard or 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?

The description provides explicit usage context: 'Use to check progress toward live trading unlock' and call format examples for filtering by strategy_id or email. However, it does not explicitly state when not to use this tool or mention alternative tools, though context from siblings suggests differentiation.

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