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Zambo AI Tools

by dev.zambo

prompt_shield

Zambo Stack — Detect prompt injection, jailbreaks, and policy bypass attempts before they reach your AI model. Two-phase analysis: instant pattern library scan (12 attack vectors) + Groq semantic analysis. Returns injection_risk 0–100, recommendation safe/review/block, and a safe rewritten version when possible. 50 free/day. Best for: 'validate user input before LLM call', 'detect jailbreak attempts', 'is this prompt safe to send to GPT?'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNo'fast' = pattern scan only (default), 'deep' = pattern + Groq semantic analysis
emailNoZambo Pass email for unlimited calls (optional)
promptYesThe user input or prompt to validate for injection/jailbreak (max 16K chars)
systemNoYour system prompt — also scanned for prompt leak attempts (optional)
contextNoDescribe your app for better contextual analysis (optional)

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description fully describes behavioral traits: two-phase analysis (pattern scan + Groq semantic), return values (injection_risk 0–100, recommendation, safe rewrite), and rate limit (50 free/day). No contradictions.

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?

The description is four sentences with front-loaded purpose. Each sentence adds value (purpose, method, returns, use cases). Slightly verbose but efficient overall.

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 5 parameters, all described, and no output schema, the description adequately covers returns and use context. It could mention error handling or quota behavior, but it is sufficiently complete for an AI agent.

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%, so parameters are already documented. The description adds some usage context (e.g., 'fast' vs 'deep' mode, email for unlimited calls) but does not provide significant new meaning beyond schema descriptions.

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's purpose: 'Detect prompt injection, jailbreaks, and policy bypass attempts before they reach your AI model.' It uses specific verbs (detect, validate) and resources (user input, LLM call), and distinguishes from siblings like prompt_lab by focusing on security validation.

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 best-use cases: 'validate user input before LLM call', 'detect jailbreak attempts', 'is this prompt safe to send to GPT?'. It lacks explicit when-not-to-use or alternative tools, but the context is clear enough for an agent to decide.

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