Zambo AI Tools

zambot_legendary_spark

Generate a LEGENDARY SPARK — the highest tier of ZAMBOT intelligence. Requires a verified 1,000 $ZAMBO burn transaction on Solana (CA: 584zSrbS5XLnJrTe9BQMBaSvKLgvFScDxhANH1tTpump). Returns: full 6-model cascade, 5-domain swarm debate, drift score, kill-shot analysis, compound move, and 48h action plan. Permanently pinned to the public Legendary Registry.

Input Schema

TableJSON Schema

Name	Required	Description
`goal`	Yes	Your goal or strategic challenge (min 10 chars). Be specific for best results.
`email`	No	Optional — your email to receive the Legendary Spark summary
`burn_txhash`	Yes	Your Solana transaction signature from burning 1,000 $ZAMBO via the SPL Token burn instruction on Solana mainnet. Mint: 584zSrbS5XLnJrTe9BQMBaSvKLgvFScDxhANH1tTpump. Base58 format, 44–90 chars. Get it from Solscan or your Phantom wallet after burning.

TDQS

A3.9/5.0

Behavior4/5

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

With no annotations, the description must disclose behavioral traits. It does so by mentioning the burn requirement, the permanence of the registry entry, and the detailed outputs. However, it does not fully describe side effects (e.g., consumption of tokens, irreversibility) beyond what is stated.

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 a single paragraph that front-loads the tool's purpose and key outputs. It is concise but could benefit from structuring the output list for readability. Every sentence contributes meaningful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of the tool (multiple outputs, prereq), the description covers all essential aspects: purpose, prerequisite, parameter hints, and a clear list of return values. Without an output schema, this level of detail is sufficient for an agent to understand what to expect.

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?

All three parameters are described in the schema (100% coverage). The description adds value by explaining the burn_txhash format and requiring exact conditions (mint address, network). It also advises specificity for the goal parameter, enhancing the schema's minimal descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates a 'LEGENDARY SPARK' – the highest tier of ZAMBOT intelligence – and lists specific outputs (6-model cascade, swarm debate, etc.). While it doesn't explicitly contrast with sibling tools like 'zambot_spark', the name and 'highest tier' language imply differentiation.

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 specifies a prerequisite (verified 1,000 $ZAMBO burn transaction) and hints at usage for strategic challenges. However, it does not provide guidance on when NOT to use this tool or suggest alternatives, leaving the agent to infer context from sibling names.

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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