Zambo AI Tools

trading_strategy_compile

Convert a natural-language trading strategy into a strict, versioned JSON schema that the trading agent enforces exactly. Define your chain (base/solana/any), mode (paper first, then live after 20 proven trades), assets, entry/exit conditions, confidence threshold, max position size %, daily drawdown hard stop, and max daily trades. Returns a strategy_id you use with trading_propose to generate proposals. Paper mode is the default — no real money, no risk. Live requires 20+ paper trades + explicit acknowledgment. CALL FORMAT: trading_strategy_compile({description: 'Mean reversion on SOL. Entry when RSI<30. Max 2% position. 5% daily drawdown hard stop.', chain: 'solana', mode: 'paper', email: 'you@example.com'})

Input Schema

TableJSON Schema

Name	Required	Description
`mode`	No	paper (default, no real money) or live (requires 20+ paper trades + acknowledgment).
`chain`	No	Execution chain. Default: 'any'.
`email`	No	Your email — ties strategy to your identity for journal tracking across sessions.
`description`	Yes	Free-text description of your strategy. Include: assets, entry/exit conditions, risk rules, timeframe, confidence threshold. The more specific, the stricter the compiled schema.

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses that paper mode involves no real money or risk, and that live mode requires prior proven trades. It also describes the output (strategy_id). It does not mention any destructive side effects, but as a compilation tool, none are expected.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is somewhat lengthy but well-structured. It front-loads the main purpose, then details parameters, modes, and example. Every sentence adds value, though it could be slightly more streamlined without losing clarity.

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?

Given the tool's complexity, the description is complete. It explains what the tool does, when to use each mode, what parameters mean, and what the output is used for next. Without an output schema, it still adequately covers return value and next steps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description adds significant meaning beyond the schema. For example, it explains that 'email' ties the strategy to identity for journal tracking, and 'description' should include specific elements. It also provides a concrete call example with real values, aiding understanding.

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 tool's purpose: 'Convert a natural-language trading strategy into a strict, versioned JSON schema.' It specifies the verb (convert), resource (trading strategy), and output (JSON schema with strategy_id). This distinguishes it from siblings like trading_propose.

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 clear usage context: paper mode is default, live mode requires 20+ paper trades and acknowledgment. It explicitly mentions that the returned strategy_id is used with trading_propose for proposals. However, it does not explicitly state when not to use this tool or mention alternatives beyond trading_propose.

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.

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