Legal jargon simplifier. Takes contract clauses, terms of service, or privacy policy text and returns: plain English explanations of legal terms, red flags, key obligations, complexity score, and risk level.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Legal text to analyze (max 20,000 chars) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for disclosing behavioral aspects. It mentions the input type and outputs but does not address potential side effects, required permissions, rate limits, or any state changes. The tool appears to be a read-only analysis, but this is not explicitly stated.
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?
The description is concise, consisting of two sentences, and uses efficient, direct language. It avoids redundancy and includes all necessary information without fluff.
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?
Since there is no output schema, the description lists the types of outputs (plain English explanations, red flags, key obligations, complexity score, risk level) but does not specify their structure, format, or how they are presented. This provides a general idea but lacks detail for a consumer to fully understand the return value.
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?
The sole parameter 'text' has a description that adds a maximum length constraint (20,000 chars), which is not present in the schema type alone. This provides additional useful context, though it could be more detailed regarding accepted formats or examples.
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's function (simplifies legal jargon), specifies input types (contract clauses, terms of service, privacy policy text), and lists the expected outputs (plain English explanations, red flags, key obligations, complexity score, risk level). It is distinct from sibling tools.
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 implicitly indicates the tool is for legal text analysis, but it does not explicitly state when to use it versus alternatives or provide any negative usage conditions. No when-not or alternative guidance is given.
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.
Several tools have overlapping functionality, such as domain_infra, company_report, and due_diligence all covering DNS/WHOIS/SSL checks. Similarly, web_scrape and scrape_structured both extract website content, and verify_email overlaps with email_audit on DNS-based email checks. This creates ambiguity in tool selection, especially for agents looking for a specific type of analysis.
All tool names use lowercase with underscores, which provides a consistent style. However, the grammatical pattern varies: some are verb-object (verify_email, currency_convert), some are noun-noun (domain_infra, site_audit), and others are adjective-noun (arabic_sentiment, brand_scout). This is not chaotic, but it lacks a rigid verb_noun convention, making it slightly less predictable.
With 25 tools, this sits at the upper boundary of what is considered 'heavy' but is still usable. The server covers a wide range of domains (Arabic NLP, web scraping, domain/email analysis, finance, faith), so the count is justified to a degree, but agents may be overwhelmed by choice. It is borderline appropriate for such a broad utility server.
The tool surface covers many common operations (scraping, DNS checks, email verification, financial data), but there are notable gaps. For example, no generic translation tool exists, only Arabizi-to-Arabic, and there is no text generation or embedding. While the set is extensive, it is not fully comprehensive for the diverse domains it touches, leaving some obvious missing operations.