Score any startup, idea, repo URL, business, or goal on a 0–100 scale with a letter grade (S/A/B/C/D), bottleneck analysis, fastest win, and a ready-to-share summary. 20 free/day. Unlimited with Zambo Pass.
| Name | Required | Description | Default |
|---|---|---|---|
| No | Zambo Pass email for unlimited scores (optional) | ||
| input | Yes | Anything to score — startup idea, repo URL, business, goal, or product (3–1000 chars). Example: 'a SaaS helping plumbers find clients with AI' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the scoring output and daily limits, but lacks details on whether it calls external APIs, if it is deterministic, authentication needs beyond email, or rate limiting behavior for unlimited tier.
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, front-loaded with core function and output, followed by usage limits. No redundant words. Every sentence serves a purpose.
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, the description adequately explains the return (score, grade, analysis, summary) and handles optional email parameter. It could be more complete by mentioning response time or typical use cases, but covers essentials.
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 value by providing an example for 'input' parameter and explaining the 'email' parameter's purpose (unlock unlimited scores). This goes beyond the schema descriptions.
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 scores any startup, idea, repo URL, business, or goal, and lists the output (0–100 scale, letter grade, bottleneck analysis, fastest win, summary). It uses a specific verb ('score') and resource, distinguishing it from sibling tools like basehawk_score or zambro_analyze.
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 by stating what it scores and the daily limit (20 free/day), but it does not explicitly guide when to use this tool vs alternatives like basehawk_score or other scoring tools, nor does it state when not to use it.
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.