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Zambo AI Tools

by dev.zambo

structlock

Enforce any schema on messy AI output, natural language, or raw text — returns clean typed JSON every time. Replaces custom parseAIResponse() functions. Supports: string, number, boolean, string[], number[], object, any. 25 free/day. Unlimited with Zambo Pass. Best for: 'extract this data from messy text', 'force JSON schema on LLM output', 'parse unstructured AI response'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rawYesThe messy text, AI output, or natural language to extract structure from (max 32K chars)
emailNoZambo Pass email for unlimited calls (optional)
schemaYesSchema definition — keys are field names, values are types: 'string' | 'number' | 'boolean' | 'string[]' | 'number[]'. Example: { name: 'string', age: 'number', tags: 'string[]' }
strictNoIf true, ambiguous fields return null instead of best-guess (default: false)
contextNoOptional hint to help extraction (e.g. 'This is a job posting')

TDQS

A4/5.0
Behavior3/5

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

No annotations provided, so description carries full burden. Discloses free tier limit (25/day), strict mode behavior (ambiguous fields return null), and supported types. However, lacks details on error handling, rate limits beyond free tier, and potential failure modes. Partially 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?

Extremely concise: 4 sentences, no fluff, front-loaded with core purpose and technical details (supported types, pricing) later. Every sentence adds value without redundancy.

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 no output schema, description compensates by stating return type ('clean typed JSON') and mentions supported types and strict mode. Covers free tier and use cases. Could add example output or error scenarios, but sufficiently complete for this complexity.

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 description coverage is 100%, so baseline is 3. The description adds minimal parameter-specific insight beyond what schema already provides (e.g., example schema, strict flag behavior). No new semantics for raw, email, context 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?

Clearly states the verb 'enforce' and resource 'schema on messy AI output', with explicit purpose: returns clean typed JSON. Distinguishes from sibling tools by specializing in structure extraction from unstructured text, which no sibling does.

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?

Explicitly lists best use cases: 'extract this data from messy text', 'force JSON schema on LLM output', 'parse unstructured AI response'. Mentions it replaces custom parseAIResponse() functions, giving context for when to use it. No explicit when-not-to-use, but context is clear enough.

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