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hitl_submit

Human-in-the-loop gate — submit any content (voice transcript, plan, action) for a human to review and optionally edit before your agent acts on it. Returns a mobile-friendly review URL the human opens, reads, edits if needed, then approves or rejects. After submitting, poll hitl_check({review_id}) every 5–10 seconds until status changes from 'pending'. When approved, use the returned content (may differ from original if human edited). WHEN TO USE: any agentic workflow where a client or team member must sign off before action — transcript review, plan approval, email draft, code change confirmation. CALL FORMAT: hitl_submit({content: transcript, context: 'This will be sent to the scheduling agent', expires_minutes: 10}).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesThe content for the human to review. Usually a voice transcript, plan, message draft, or list of proposed actions. Max 10,000 chars.
contextNoWhat will happen with this content after approval — shown to the human so they understand the stakes. E.g. 'This transcript will be sent to the scheduling agent to book a meeting.'
expires_minutesNoHow long the review link stays active. Default: 10 minutes. Max: 60 minutes.

TDQS

A4.5/5.0
Behavior4/5

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

No annotations provided, so description carries full burden. Clearly explains flow: submit, get URL, poll with hitl_check, handle edits. Could mention side effects (creates review request) but overall transparent.

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?

Well-organized into usage guidelines, call format, and explanation. Concise but includes necessary details. Could be slightly tighter without losing clarity.

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?

No output schema, but description explains return of review URL and references hitl_check for polling. Covers the main flow, though exact return format could be more explicit. Adequate for agent understanding.

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% with descriptions. Description adds context about context parameter stakes, expires_minutes defaults and max, content max chars. Adds value beyond schema.

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?

Describes submitting content for human review, with specific examples (voice transcript, plan, action). Distinguishes from sibling hitl_check which polls for results.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit 'WHEN TO USE' section listing applicable workflows, and shows CALL FORMAT with example. Implicitly indicates when not to use (if no human review needed), covers alternatives.

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.

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