Generate a cryptographic proof chain: Zambo Spark (strategy) → x711 execution log. Returns a single verifiable SHA-256 provenance hash anchoring your strategy to its execution.
| Name | Required | Description | Default |
|---|---|---|---|
| spark_hash | Yes | ZAMBOT Spark hash | |
| execution_log | No | Summary of x711 tool calls made |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only states that it generates a proof and returns a hash, without disclosing side effects, storage behavior, authorization requirements, or reversibility. Minimal disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that concisely conveys the purpose, inputs, and output without any redundancy. It is front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only 2 parameters and no output schema, the description explains the output (SHA-256 hash) sufficiently. However, it lacks details on return format and differentiation from similarly named siblings like zambot_chain. Still, it covers the essentials for a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds context by naming the concepts 'Zambo Spark' and 'x711 execution log', which helps understand the parameters' roles beyond the schema descriptions. Enhances clarity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a cryptographic proof chain linking a Zambo Spark strategy to an execution log and returns a SHA-256 hash. It specifies the inputs and output, distinguishing it from many siblings, though some like zambot_chain may overlap in concept.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for anchoring a strategy to its execution, but it does not provide explicit when-to-use or when-not-to-use guidance nor mention alternatives. The context is clear but lacks explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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 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.
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.
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.