Zambo AI Tools

workout_build

Build a complete personalized workout plan with exercises, sets, reps, and progression — based on your goal and equipment. Returns a structured weekly schedule with warm-up, main sets, and cooldown. Use when user says 'make me a workout', 'gym plan', 'home workout', 'how should I train'.

Input Schema

TableJSON Schema

Name	Required	Description
`goal`	No	Training goal: strength, muscle, fat_loss, endurance, mobility, general_fitness. Default: general_fitness.
`focus`	No	Optional body part focus: upper, lower, full_body, push_pull_legs, chest, back.
`equipment`	No	Available equipment: none (bodyweight), dumbbells, barbell, gym (full), resistance_bands. Default: gym.
`days_per_week`	No	Training days per week (2–6). Default: 4.
`fitness_level`	No	beginner, intermediate, advanced. Default: intermediate.
`duration_minutes`	No	Session length in minutes (20–90). Default: 45.

TDQS

A4.3/5.0

Behavior3/5

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

No annotations provided, so description carries full burden. It describes the output structure (weekly schedule with warm-up, main sets, cooldown) but does not disclose behavioral traits like default handling, error cases, or limitations. While not misleading, it lacks depth on behavior beyond the basic return format.

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?

Two sentences, front-loaded with action and result. Efficiently communicates purpose, output, and usage triggers. Could be slightly more compact, but no wasted words.

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 explains the return format (weekly schedule with warm-up, main sets, cooldown) and covers the key customization parameters. It misses some nuances like default values and validation, but overall sufficient for an agent to understand the tool's capabilities.

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%, so baseline is 3. The description adds value by stating the plan is based on 'your goal and equipment', tying parameters to personalization. It also includes usage examples that imply parameter use. This exceeds the schema alone.

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 uses the specific verb 'Build' and resource 'complete personalized workout plan', clearly stating the tool's output. It includes examples of user queries that trigger this tool, distinguishing it from siblings (no similar tools in list).

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

Usage Guidelines5/5

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

Explicitly provides usage triggers: 'Use when user says...' with multiple example phrases. Also mentions customization based on goal and equipment, giving clear context for when to invoke.

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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