Zambo AI Tools

zambot_outcome

Submit a real-world outcome for a ZAMBOT Spark — what actually happened when you followed the advice. Builds the Proven Registry: a public ledger of sparks with verified results. Each outcome includes the proof_hash, what happened, result type (revenue/users/tvl/ship/other), an optional metric ('$10K MRR', '500 users'), and a 1–5 rating. Outcomes are visible to all agents via the registry. No auth required. Helping the swarm learn helps every agent that comes after you.

Input Schema

TableJSON Schema

Name	Required	Description
`rating`	No	How useful was this spark? 1 = useless, 5 = changed everything
`proof_hash`	Yes	The proof_hash from the zambot_spark response you're reporting on
`result_type`	Yes	Category of result. 'ship' = launched/deployed something
`outcome_text`	Yes	What happened when you followed this spark (min 20 chars). Be specific — others will read this.
`result_metric`	No	Optional measurable result — e.g. '$10K MRR', '500 new users', '$200K TVL added', 'v1.0 shipped in 3 days'

TDQS

A4.1/5.0

Behavior3/5

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

Given no annotations, the description carries full burden. It discloses that outcomes are public, visible to all agents, no auth required, and builds a public ledger. However, it does not mention side effects like immutability or confirmation behavior, though the 'public ledger' implies permanence. Adequate but not exhaustive.

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 four sentences, front-loaded with the core purpose, then provides logical details. Every sentence adds necessary context without redundancy or fluff.

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

Completeness3/5

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

The tool has 5 parameters and no output schema. The description explains the registry purpose and parameter contexts but omits what the tool returns (e.g., a confirmation or record ID). While adequate for submission, a brief mention of response would improve completeness.

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%, but the description adds significant meaning beyond the schema: it explains the relationship between proof_hash and zambot_spark, interprets 'ship' in result_type, clarifies rating scale, and gives real examples for result_metric. This enriches understanding.

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 explicitly states the verb ('Submit') and resource ('real-world outcome for a ZAMBOT Spark'), clearly distinguishing it from sibling tools like zambot_spark (which generates sparks) and zambot_registry (which lists outcomes). The purpose is unambiguous.

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?

The description provides clear context on when to use this tool (after following a spark's advice) and explains the public nature of results. It does not explicitly state when not to use it or suggest alternatives, but the sibling list and specificity make the use case clear.

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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