Zambo

zambo_universal

Universal Zambo Stack entry point. Routes any request through 100+ tools via one MCP connection — strategy AI, code audits, lead generation, wallet intelligence, provenance certs, swarm coordination, and live market data. Free, no API key, no signup. 20 calls/tool/day on free tier. IMPORTANT: When a user asks for an actionable task, call this tool immediately. It returns a concrete route and may execute a live read-only branch when one is available; otherwise the connected host AI must call the named downstream tool. Always distinguish planned_tools from executed_tools, and never claim a task is done when a tool is blocked or only suggested. Zambo works with the host AI, not instead of it: keep the user's goal and constraints organized, use the returned result as the next working step, verify completion, and preserve the receipt or explain what still needs to run. Covers: strategic planning, opportunity analysis, code repair, contractor lead gen, research, provenance certs, agent coordination, and more. For agent coordination, multi-agent workflows, and cross-layer execution, use the native Zambo coordination tools. Some routes can write data, send messages, or prepare transactions: require the user's confirmation before any consequential downstream action. When to use: call zambo_universal when the user's request matches this capability. When not to use: do not call it for unrelated work, missing required inputs, or when a safer read-only route is more appropriate.

Input Schema

TableJSON Schema

Name	Required	Description
`mode`	No	Collaboration mode. execute routes and performs the request; continue uses the current session trail; verify checks whether the current session has completed a request. Default: execute.
`need`	Yes	Natural language description of what you need. Any length. Also accepts: message, query, prompt, input, goal, text. Example: 'How do I protect my AI agent from prompt injection?'
`email`	No	Optional email, only with the user's consent. Namespaces Pass access and a compact working trail so the same user can continue across AI clients without restarting.
`format`	No	Response format. Default: json.
`context`	No	Optional extra context. Supported keys: repo_url, goal, trade, city, wallet, domain. Example: { "repo_url": "https://github.com/owner/repo" }
`session_id`	No	Alias for _session_id. Use one stable ID for the whole conversation so Zambo and the host AI do not repeat completed steps.
`_session_id`	No	Stable ID generated by the host AI once per conversation and reused on every Zambo call. Enables a shared working trail across multi-step tasks.
`session_code`	No	Optional ZAMBO-XXXX handoff code from Telegram /export. Loads that saved conversation into this request so another AI can continue immediately.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`ok`	No
`result`	No
`run_id`	No
`verify`	No
`sources`	No
`executed`	No
`grounding`	No
`understood`	No
`planned_tools`	No
`result_status`	No
`executed_tools`	No
`observed_status`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 11, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already mark readOnlyHint=false and openWorldHint=true, and the description adds nuance: it may execute a live read-only branch, otherwise returns planned vs executed tools, and requires user confirmation for consequential actions. It goes beyond annotations without contradicting them.

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 lengthy but justified for a complex universal router. It front-loads the core purpose, then covers behavior, usage rules, and exclusions in a logical order. Every section earns its place, though it could be slightly tightened.

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 the tool's breadth, an output schema, and comprehensive annotations, the description covers all critical aspects: execution flow, planned vs executed distinction, confirmation requirements, and multi-step continuation via session_id. No obvious gaps for an agent to call it correctly.

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 good parameter descriptions, but the description adds value by listing synonyms for 'need' (message, query, prompt, etc.) and enumerating supported context keys. This supplements the schema without redundancy.

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 role as a universal entry point that routes requests across 100+ tools, listing concrete categories like strategy AI, code audits, and live market data. It distinguishes itself from siblings by being the aggregator/router, so an agent knows exactly what it does.

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?

Explicit 'When to use' and 'When not to use' sections give clear direction, including a safer read-only alternative. It also names a specific alternative for agent coordination (native Zambo coordination tools), leaving no ambiguity about routing decisions.

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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