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Zambo AI Tools

by dev.zambo

credithunt

Zambo Stack — Live, verified index of every AI and cloud startup credit program available right now. 29+ active programs including AWS Activate, Google Cloud, Azure, OpenAI, Anthropic, Vercel, Supabase, Modal, Groq, Replicate, and more. Verified daily — dead links auto-removed. Pass your tech stack to get matched recommendations. Free, no auth.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stackNoYour tech stack for matched recommendations. Example: ["openai","vercel","aws"]. Leave empty to get all programs.
stageNoYour stage: solo (1 person), early (2–10), growth (10+). Default: solo.
min_valueNoMinimum credit value in USD to filter by (optional). Example: 5000

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses key behaviors: 'Free, no auth', 'Verified daily — dead links auto-removed', and that it's a live index. It does not mention any destructive actions, which is appropriate for a read-only tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is moderately concise and front-loaded with the tool's name and value proposition. It includes key details but could be slightly shorter. Every sentence adds information, though some repetition exists.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simple purpose (listing credit programs) and the thorough schema descriptions, the tool description provides sufficient context. It covers what the tool does, how to use it, and key constraints. Without an output schema, the description doesn't detail return values, but that is acceptable for a list tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All three parameters have descriptions in the input schema (100% coverage), so the baseline is 3. The tool description adds minimal value beyond the schema, only restating the purpose of the 'stack' parameter. The schema already includes examples, so no significant addition.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: a live, verified index of AI/cloud startup credit programs. It specifies the action ('get matched recommendations') and resource ('credit programs'), and distinguishes it from siblings by focusing on credit programs rather than general startup tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use (pass tech stack for recommendations) and provides an alternative (leave empty to get all programs). It does not explicitly state when not to use or compare to siblings, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.5/5.0
Disambiguation3/5

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 Consistency3/5

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.

Tool Count2/5

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.

Completeness5/5

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.

Resources