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Zambo AI Tools

by dev.zambo

regex_build

Describe what you need to match and get a working regex — with explanation, edge cases, and test examples in your language. No more Stack Overflow rabbit holes. Returns pattern, explanation in plain English, and ready-to-paste code snippet. Use when user says 'regex for', 'pattern to match', 'extract X from string', 'validate email/phone/URL'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
flagsNoRegex flags to apply: global, multiline, case_insensitive. E.g. 'global,case_insensitive'.
examplesNoExamples of strings it should and should NOT match. E.g. 'Should match: (555) 123-4567, 555-123-4567. Should NOT match: 12345'.
languageNoProgramming language for the code snippet: javascript, python, go, rust, java, ruby, php. Default: javascript.
descriptionYesWhat the regex should match. Be specific. E.g. 'US phone numbers with or without country code', 'email addresses', 'any URL', 'dates in MM/DD/YYYY format'.

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 the output: pattern, plain English explanation, and code snippet. No side effects mentioned, but the tool is generative and not destructive, so this is adequate.

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?

Two concise sentences that front-load the purpose and include usage triggers. 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?

For a generative tool with no output schema, the description adequately covers purpose, usage, and return format. Could mention language-specific behavior, but schema covers language parameter.

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 description coverage is 100% with good parameter descriptions. The tool description adds value by reinforcing usage and output format, but the schema already does heavy lifting. Just above baseline due to extra context.

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 regex patterns from a description, including explanation, edge cases, and code snippets. It uniquely identifies the tool's function among many 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?

The description explicitly lists triggers for using the tool ('regex for', 'pattern to match', etc.), providing clear context. It does not specify when not to use, but the triggers are sufficient for an AI agent.

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