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Zambo AI Tools

by dev.zambo

thread_craft

Turn any idea, topic, or article into a viral-ready X (Twitter) thread — numbered, hooky, and optimized for engagement. Returns each tweet numbered with the hook, body tweets, and CTA. Ready to copy-paste and post. Use when user says 'write a thread about', 'make this into a thread', 'Twitter thread on X'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toneNoTone: educational, controversial, storytelling, listicle, hot_take. Default: educational.
topicYesWhat the thread is about. Can be an idea, URL, article summary, or topic. Be specific for best results.
lengthNoNumber of tweets in the thread (3–15). Default: 8.
accountNoOptional: describe your account/audience (e.g. 'AI startup founder audience', 'crypto traders'). Tailors the thread voice.

TDQS

A4.1/5.0
Behavior4/5

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

No annotations provided, but the description discloses expected behavior: returns numbered tweets with hook, body, and CTA. It doesn't mention side effects or destructive actions, which is acceptable for a generative tool. Could improve by noting that content is AI-generated and may need review.

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 concise and front-loaded with the main purpose, followed by output format and usage hints. Each sentence adds value, though the usage examples could be slightly more compact.

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 content generation tool with 4 parameters and no output schema, the description covers purpose, output format, and usage scenarios. It lacks error handling info but is otherwise complete.

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 the description adds moderate value by explaining the output context but not per-parameter details. Baseline 3 is appropriate as the schema already describes parameters well.

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: turning ideas/topics/articles into viral-ready X threads. It uses specific verbs ('turn into', 'returns') and specifies the resource ('X Twitter thread'). No sibling tools serve the same function, so differentiation is implicit.

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 usage cues ('Use when user says...') and lists trigger phrases. It doesn't mention when not to use or alternative tools, but the positive guidance is clear and sufficient given no overlap with siblings.

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