Zambo AI Tools

meal_plan

Generate a personalized meal plan with recipes, macros, and a shopping list — in seconds. Specify dietary restrictions, budget, cuisine preferences, and number of days. Returns structured daily meals with prep times. Use when user says 'meal plan', 'what should I eat', 'plan my meals', 'make me a diet plan'.

Input Schema

TableJSON Schema

Name	Required	Description
`days`	No	Number of days to plan (1–14). Default: 5.
`goal`	No	Health goal: weight loss, muscle gain, maintenance, energy. Default: balanced.
`budget`	No	Weekly food budget hint. E.g. '$50/week', '$100/week'. Default: moderate.
`dietary`	No	Dietary restrictions or style: vegan, vegetarian, keto, paleo, gluten-free, halal, etc. Default: none.
`servings`	No	People to cook for. Default: 1.
`preferences`	No	Cuisine preferences or dislikes. E.g. 'love Mexican, hate fish'.

TDQS

A4/5.0

Behavior3/5

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

No annotations are provided, so the description must carry the behavioral burden. It mentions returns structured data with prep times, but does not disclose side effects (e.g., persistence), authorization needs, or whether it is a pure generation (read-only). This is adequate but not thorough.

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?

The description is extremely concise: two sentences packing purpose, outputs, inputs, and usage triggers. Every sentence adds value with no fluff, and the key info is front-loaded.

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 mentions return type ('structured daily meals with prep times'), which is helpful. It also hints at defaults via examples. However, it doesn't explain error handling, parameter constraints (like days 1-14), or fallback behavior for missing inputs.

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 coverage is 100% with descriptions for all 6 parameters. The description only summarizes the main inputs ('dietary restrictions, budget, cuisine preferences, and number of days'), adding no new meaning beyond the schema. Baseline 3 is appropriate.

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 generates personalized meal plans with specific outputs (recipes, macros, shopping list) and lists key input parameters. It distinguishes itself from sibling tools like 'fridge_forge' and 'workout_build' by focusing on meal planning.

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 use cases with example user phrases ('meal plan', 'what should I eat', etc.), making it easy for an agent to match intents. However, it lacks when-not-to-use guidance or alternatives, such as 'fridge_forge' for ingredient-based plans.

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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