Brief any new AI agent on the complete Zambo + x711 stack. Returns an onboarding package: stack capabilities, tool inventory, and recommended workflows for the agent's purpose.
| Name | Required | Description | Default |
|---|---|---|---|
| focus | No | Which part of the stack to emphasize | |
| agent_purpose | Yes | What this agent is designed to do |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It fails to disclose whether the operation is read-only, modifies any state, or requires specific permissions. The description only hints at returning a package, but does not assure safety or side-effects.
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?
Description is a single sentence that conveys the core purpose without fluff. It is appropriately concise, though breaking into two sentences could improve readability.
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?
Without an output schema, the description adequately explains the return value: an onboarding package with three components. Given only two simple parameters, this provides sufficient context for an agent to use the 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 coverage is 100%, so baseline is 3. Description adds minimal value beyond schema: it mentions 'for the agent's purpose' but does not elaborate on the 'focus' parameter or provide additional context for either parameter.
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?
Description clearly states the verb 'brief' and the target resource 'new AI agent on the complete Zambo + x711 stack'. It differentiates from sibling tools like 'agent_brief' by specifying an onboarding package, which is explicit and distinct.
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?
No guidance on when to use this tool versus alternatives such as 'agent_brief' or 'zambo_brief'. The description only states what it does, not when it should be chosen over siblings.
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.