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Zambo Universal

zambo_universal
Destructive

Universal Zambo entry point for routing natural-language requests across supported Zambo tools through one MCP connection. Covers strategy, code audits, lead generation, wallet intelligence, provenance certificates, swarm coordination, and live market data. Returns a route, execution state, downstream tool results when available, and receipt information.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoCollaboration 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.
needYesNatural 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?'
emailNoOptional 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.
formatNoResponse format. Default: json.
contextNoOptional extra context. Supported keys: repo_url, goal, trade, city, wallet, domain. Example: { "repo_url": "https://github.com/owner/repo" }
rememberNoOptional explicit fact to persist in this stable session. It is returned by mode:'verify'; only use after the user asks you to remember it or clearly consents.
session_idNoAlias for _session_id. Use one stable ID for the whole conversation so Zambo and the host AI do not repeat completed steps.
_session_idNoStable 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_codeNoOptional ZAMBO-XXXX handoff code from Telegram /export. Loads that saved conversation into this request so another AI can continue immediately.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNo
resultNo
run_idNo
verifyNo
sourcesNo
executedNo
groundingNo
understoodNo
planned_toolsNo
result_statusNo
executed_toolsNo
observed_statusNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
      +  {
      +    "mode": "execute",
      +    "need": "Protect my AI agent from prompt injection"
      +  }
      +]
    • addedInput schema / properties / remember
      Added value: +{
      +  "description": "Optional explicit fact to persist in this stable session. It is returned by mode:'verify'; only use after the user asks you to remember it or clearly consents.",
      +  "maxLength": 1000,
      +  "type": "string"
      +}
  2. Changed15 schema fields changed
    • addedOutput schema / properties / executed
      Added value: +{
      +  "type": "boolean"
      +}
    • addedOutput schema / properties / executed_tools
      Added value: +{
      +  "items": {
      +    "type": "string"
      +  },
      +  "type": "array"
      +}
    • addedOutput schema / properties / grounding
      Added value: +{
      +  "type": "string"
      +}
    • addedOutput schema / properties / observed_status
      Added value: +{
      +  "type": "string"
      +}
    • addedOutput schema / properties / ok
      Added value: +{
      +  "type": "boolean"
      +}
    • addedOutput schema / properties / planned_tools
      Added value: +{
      +  "items": {
      +    "type": "string"
      +  },
      +  "type": "array"
      +}
    • removedOutput schema / properties / response
      Removed value: -{
      -  "type": "string"
      -}
    • addedOutput schema / properties / result
      Added value: +{
      +  "type": "string"
      +}
    • addedOutput schema / properties / result_status
      Added value: +{
      +  "type": "string"
      +}
    • removedOutput schema / properties / routed_to
      Removed value: -{
      -  "type": "string"
      -}
    • addedOutput schema / properties / run_id
      Added value: +{
      +  "type": "string"
      +}
    • addedOutput schema / properties / sources
      Added value: +{
      +  "items": {
      +    "type": "object"
      +  },
      +  "type": "array"
      +}
    • removedOutput schema / properties / tools_invoked
      Removed value: -{
      -  "items": {
      -    "type": "string"
      -  },
      -  "type": "array"
      -}
    • addedOutput schema / properties / understood
      Added value: +{
      +  "type": "string"
      +}
    • addedOutput schema / properties / verify
      Added value: +{
      +  "additionalProperties": true,
      +  "type": "object"
      +}
  3. Changed1 schema field changed
    • addedInput schema / properties / email
      Added value: +{
      +  "description": "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.",
      +  "type": "string"
      +}
  4. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "additionalProperties": true,
      +  "properties": {
      +    "response": {
      +      "type": "string"
      +    },
      +    "routed_to": {
      +      "type": "string"
      +    },
      +    "tools_invoked": {
      +      "items": {
      +        "type": "string"
      +      },
      +      "type": "array"
      +    }
      +  },
      +  "type": "object"
      +}
  5. Changed4 schema fields changed
    • addedInput schema / properties / _session_id
      Added value: +{
      +  "description": "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.",
      +  "type": "string"
      +}
    • addedInput schema / properties / mode
      Added value: +{
      +  "description": "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.",
      +  "enum": [
      +    "execute",
      +    "continue",
      +    "verify"
      +  ],
      +  "type": "string"
      +}
    • addedInput schema / properties / session_code
      Added value: +{
      +  "description": "Optional ZAMBO-XXXX handoff code from Telegram /export. Loads that saved conversation into this request so another AI can continue immediately.",
      +  "type": "string"
      +}
    • addedInput schema / properties / session_id
      Added value: +{
      +  "description": "Alias for _session_id. Use one stable ID for the whole conversation so Zambo and the host AI do not repeat completed steps.",
      +  "type": "string"
      +}
  6. Changed2 schema fields changed
    • changedInput schema / properties / need / description
      Previous value: -"Natural language description of what you need (5–2000 chars). Example: 'How do I protect my AI agent from prompt injection?'"New value: +"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?'"
    • removedInput schema / properties / need / minLength
      Removed value: -5
  7. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already provide destructiveHint=true, and the description adds that it returns execution state and downstream tool results, implying it can execute downstream tools. It doesn't elaborate on potential side effects or permissions, but the annotation covers the destructive nature; still, some added context on downstream execution would be valuable.

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?

The description is three sentences with no fluff. It front-loads the core purpose in the first sentence, lists domains in the second, and summarizes outputs in the third. Every sentence earns its place.

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 tool with 9 parameters, an output schema, and rich annotations, the description covers purpose, scope, and returns. The main gap is the lack of guidance on when to use this router versus specific siblings, but the output schema and annotations fill most of the remaining context.

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%, and the description adds no parameter-specific information. The description mentions 'natural-language requests' which maps to the 'need' parameter, but this is already clear from the schema; the baseline of 3 applies.

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 it is a universal entry point for routing natural-language requests across supported Zambo tools, using specific verbs and resources. It enumerates the domains it covers, which distinguishes it from the specific sibling tools like ghost_audit_report or live_price.

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

Usage Guidelines3/5

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

The description implies usage: it's the universal entry point, so you'd use it when you have a natural-language request and aren't sure which specific tool to call. However, it doesn't explicitly state when to avoid it or when to prefer a sibling tool like leadsignal directly, leaving this to inference.

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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