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trading_risk_gate

Pure-math position risk check — NO AI, NO hallucination possible. Hard-enforces: position size vs max_position_pct, daily P&L vs daily_drawdown_pct, confidence vs threshold. Returns PASS or REJECT with exact mathematical reason. Must be called before every trade proposal. CALL FORMAT: trading_risk_gate({proposed_size_usd: 50, portfolio_value_usd: 2500, daily_pnl_usd: -30, confidence: 0.85, risk: {max_position_pct: 2, daily_drawdown_pct: 5, confidence_threshold: 0.80}})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
riskYesRisk parameters from your strategy schema.
confidenceYesModel confidence 0–1 (e.g. 0.85 = 85%).
daily_pnl_usdNoP&L so far today in USD (negative = loss). Default: 0.
proposed_size_usdYesDollar size of the proposed trade.
portfolio_value_usdYesTotal portfolio value in USD.

TDQS

A4.5/5.0
Behavior4/5

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

Clearly describes deterministic mathematical checks, no hallucination, and exact return format (PASS/REJECT with reason). With no annotations, the description effectively communicates safety and behavior.

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?

Every sentence serves a purpose: purpose, rules, return, usage requirement, and example. No redundant text; well-structured and front-loaded.

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?

Covers purpose, input parameters (with example), behavior, return value, and usage mandate. No output schema needed; return description is sufficient.

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 covers all parameters (100% coverage). Description adds a concrete call format example and links parameters to the enforcement rules, enhancing understanding 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?

States it is a pure-math position risk check that enforces three specific rules and returns PASS/REJECT. Distinguishes from sibling tools like trading_propose by emphasizing it is a pre-trade gate with no AI.

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 'Must be called before every trade proposal,' providing clear when-to-use guidance. Does not name sibling alternatives for comparison but implies a sequential workflow.

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