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Zambo AI Tools

by dev.zambo

prompt_lab

Test and compare a prompt across multiple AI models — get outputs side-by-side with quality scores. Returns each model's response with a consistency score and recommendation for which model handles this prompt type best. Use when user says 'test this prompt', 'which model is best for', 'compare how models respond to', 'prompt engineering'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoComma-separated models to test. Options: llama-70b, llama-8b, compound, compound-mini, qwen. Default: tests all 4 main models.
promptYesThe prompt to test across models. Any length.
systemNoOptional system prompt to include with each test.
scoringNoInclude quality scoring and model recommendation. Default: true.

TDQS

A4.4/5.0
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses that outputs include consistency scores and a recommendation, and mentions default behavior for models and scoring. Could be more explicit about read-only nature or side effects, but adequate for a comparison tool.

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?

Front-loaded with main purpose, followed by return format and usage triggers. Every sentence adds value; no wasted words. Concise yet informative.

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?

With no output schema and 4 parameters, the description covers return format (side-by-side, scores), all parameters, and usage scenarios. Lacks mention of error handling or rate limits, but is complete enough for effective agent use.

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%, baseline 3. Description adds value by stating defaults for 'models' and 'scoring' and explaining the model options. This goes beyond 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?

Description clearly states the tool tests and compares prompts across AI models with side-by-side outputs and quality scores. The verb 'test' and 'compare' with 'prompt' and 'models' makes the purpose specific and distinct from sibling tools.

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?

Provides explicit trigger phrases like 'test this prompt', 'which model is best for', etc., giving clear context for when to use. Lacks explicit when-not-to-use or alternative tools, but the examples are sufficient.

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