Zambo AI Tools

image_analyze

Analyze any image with AI vision — describe what's in it, extract text (OCR), review code/errors from screenshots, identify subjects, read charts. Accepts any public image URL. Works on photos, screenshots, diagrams, UI mockups, code error images, documents, charts. Use when a user shares an image and asks: 'what is this?', 'describe this', 'read the text in this', 'what's wrong with this code?', 'who is this?', 'what does this chart show?'. Free: 10/day. Zambo Pass: unlimited.

Input Schema

TableJSON Schema

Name	Required	Description
`mode`	No	Analysis mode: 'describe' (general description), 'ocr' (extract text), 'code' (analyze code/errors), 'style' (artistic style analysis). Default: auto-detect from question.
`question`	No	Optional specific question about the image. Examples: 'What text does this contain?', 'What error is shown?', 'Describe the subject', 'What style is this art?'. Defaults to a general description.
`image_url`	Yes	Public URL of the image to analyze. Examples: https://example.com/photo.jpg, a GitHub raw URL, an Imgur link, any direct image URL.

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries full burden. It discloses capabilities (describe, OCR, code, style), input requirements (public image URL), and rate limits (10/day free, unlimited with Zambo Pass). It doesn't cover error handling or unsupported formats, but overall transparency is good.

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

Conciseness5/5

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

The description is four sentences, front-loaded with key capabilities, then input requirements, use cases, and limits. Every sentence adds value without redundancy.

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

Completeness4/5

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

Given no output schema, the description implies return types by listing analysis modes (description, text, code analysis). It covers input, usage, and limits. Missing details on output format, but sufficient for common usage.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining each parameter's intent (e.g., mode examples, question examples, URL examples) beyond the schema descriptions, making them more actionable.

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: analyzing images with AI vision, listing specific capabilities (describe, OCR, code review, etc.). It distinguishes from siblings like 'image_generate' by focusing on analysis rather than generation.

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 provides explicit usage scenarios (e.g., user asks 'what is this?', 'read the text'), and mentions free tier limits. It doesn't explicitly exclude alternatives, but no competing image analysis tools are present among siblings, making the guidance 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

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