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Zambo AI Tools

by dev.zambo

pdf_extract

Fetch and extract text content from any public URL — PDF documents, web pages, articles, docs. Returns the extracted text, word count, and a summary. Works on research papers, contracts, articles, reports. Use when user says 'read this PDF', 'extract from this URL', 'summarize this document', 'what does this say'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPublic URL to the document or webpage to extract and summarize.
max_charsNoMax characters of raw text to return (default: 4000, max: 8000).
summarizeNoAlso generate an AI summary. Default: true.

TDQS

A4.2/5.0
Behavior3/5

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 states the tool works on public URLs and returns text, word count, and summary, but fails to disclose rate limits, authentication requirements, or error handling for inaccessible URLs.

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 two concise sentences with no fluff. The first sentence front-loads the core action and resource, and the second provides clear usage triggers.

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 the 3 parameters, no output schema, and no annotations, the description is fairly complete. It covers input, process, and output. However, it could mention supported file size limits or that only text is extracted (not images).

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 the return fields (text, word count, summary) and implying the purpose of max_chars and summarize, which the schema only names.

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 uses specific verbs ('Fetch and extract') and resources ('text content from any public URL') and explicitly lists supported document types (PDF, web pages, articles, docs). It clearly distinguishes from sibling tools by focusing on extraction and summarization from URLs.

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 use cases ('when user says read this PDF, extract from this URL, summarize this document') but does not mention when not to use the tool or suggest alternatives like doc_intel for structured data.

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