Legitimacy scanner — checks if a site and/or GitHub repo is legit or vibecoded slop. Returns a 0-100 score, letter grade, vibe score, code score, green flags, red flags, and a plain-English verdict. Pass a URL and optionally a GitHub link. Free, no auth.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The site URL to check (e.g. 'https://someproject.xyz') | |
| github | No | Optional GitHub repo URL (e.g. 'https://github.com/org/repo') for code quality analysis |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It details the return values: '0-100 score, letter grade, vibe score, code score, green flags, red flags, and a plain-English verdict.' Also states it's free and requires no auth, which is helpful for an agent.
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?
Two sentences: first states purpose and output, second gives usage and auth. No wasted words, front-loaded with essential information.
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?
For a simple 2-parameter tool without output schema, the description explains all return values. It doesn't mention error handling or rate limits, but given the straightforward nature and 'free, no auth' note, it's mostly complete.
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?
Both parameters are fully described in the schema (100% coverage). The description adds minimal extra meaning beyond what's in the schema, so baseline 3 is appropriate.
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's purpose: 'Legitimacy scanner — checks if a site and/or GitHub repo is legit or vibecoded slop.' It uses specific verbs and resources, distinguishing it from sibling tools like zambo_score or zambot_verify.
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 tells users to 'Pass a URL and optionally a GitHub link' and notes it's 'Free, no auth.' While it doesn't explicitly state when not to use, the context is clear and the tool's purpose is well-defined.
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.