Alias for leadsignal — AI lead generation for contractors. Provide trade and city, get 5 qualified leads with contact info in under 60 seconds. Supports 24 trades and any US city. 3 free searches/day.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | City and optional state. Example: 'Chicago', 'Austin TX' | |
| trade | Yes | Trade or service type. Example: 'plumber', 'HVAC', 'electrician', 'roofer' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses output (5 qualified leads with contact info), speed (under 60 seconds), and usage limit (3/day). Could be more specific about output format or data handling, but covers key behavioral aspects.
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?
Three sentences with no wasted words. Front-loaded with alias and purpose, followed by key details. Efficient and easy to parse.
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 no output schema, description adequately explains what to expect: 5 leads with contact info, and constraints (trades, cities, limit). Could mention output format or handling of limits, but it's mostly complete 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 coverage is 100% with descriptions for both parameters. Description adds value by providing concrete examples ('plumber', 'Chicago'), clarifying the combination and typical usage beyond the schema.
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 it's an alias for leadsignal and specifies the function: AI lead generation for contractors by providing trade and city. It differentiates from siblings by clarifying the alias relationship, and the action is concrete.
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?
Provides clear context on when to use: for contractor lead generation. Mentions supported trades (24) and US cities, and daily limit (3 free searches). However, doesn't explicitly state when not to use or compare to alternatives.
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.