Zambo AI Tools

model_oracle

Zambo Stack — Get a ranked recommendation of AI models for your specific task, token budget, and cost constraints. Covers Groq, Anthropic Claude, OpenAI, Google Gemini — 10 models tracked with current June 2026 pricing. Add use_case for a personalized Groq-powered insight. 30 free/day. Best for: 'which model should I use for summarization?', 'cheapest model for classification', 'compare GPT-4o vs Claude Sonnet for coding'.

Input Schema

TableJSON Schema

Name	Required	Description
`task`	Yes	Task type — one of: reasoning, coding, classification, summarization, extraction, chat, long_context, agentic, multimodal, analysis, writing, structured_output
`email`	No	Zambo Pass email for unlimited calls (optional)
`budget`	No	'low' = under $0.50/1M tokens, 'medium' = under $3/1M tokens, 'any' = all models (default: 'any')
`tokens`	No	Expected tokens per call — used to compute cost estimate per call (optional)
`use_case`	No	Describe what you're building for a personalized recommendation (e.g. 'classifying customer support tickets at 10K/day volume')

TDQS

A3.9/5.0

Behavior3/5

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

No annotations provided, so description must carry weight. Discloses rate limit (30 free/day) and pricing accuracy (June 2026). Does not mention side effects or permissions, but minimal for a read-like tool.

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?

Four sentences, front-loaded with purpose, no fluff. Could be slightly more structured (e.g., bullet list) but efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, but description does not mention return format (list of models? scores?). For a recommendation tool, output structure is critical missing context.

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 explaining use_case parameter ('personalized Groq-powered insight') and clarifying budget default ('any').

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 tool provides ranked model recommendations for given task, token budget, and cost constraints. Lists specific providers and use cases, making purpose distinct from siblings.

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?

Includes best-for examples and notes on free tier (30/day). Does not explicitly state when not to use or mention alternatives, but context is clear.

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

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