Extractive text summarization. Scores sentences by keyword frequency and returns the most important ones in original order, plus top 10 keywords.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to summarize (max 50,000 characters) | |
| max_sentences | No | Number of key sentences to extract (default: 5) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool uses extractive summarization (no paraphrasing), returns sentences in original order, and provides top keywords, which are helpful behavioral traits. However, it does not mention potential limitations like sensitivity to punctuation, handling of very short texts, or whether the output is deterministic, which could be important for user expectations.
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, one sentence, and front-loads the key action ('Extractive text summarization') followed by method and output. Every word earns its place 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?
Given the tool's moderate complexity (2 params, no nested objects, no output schema), the description covers purpose, method, output, and keyword feature. It lacks details on edge cases or output format, but the absence of an output schema means a brief description might suffice. It is complete for basic usage.
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%, and the description adds context by explaining that it scores sentences by keyword frequency and returns top sentences, which implies how max_sentences is used. The description does not add new details about parameters beyond what the schema already states (e.g., default for max_sentences is in schema). Since schema is thorough, baseline 3 is appropriate.
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 it performs extractive text summarization, specifies the method (keyword frequency), and notes it returns important sentences in original order plus top 10 keywords. This distinguishes it from siblings like 'legal_simplifier' and 'detect_language' by focusing on extractive summarization rather than translation or simplification.
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 for summarization tasks but does not explicitly state when to use this versus alternatives like legal_simplifier or detect_language. It does not provide exclusions or prerequisites, such as requiring English text or being suitable for short texts. However, the clear purpose offers some guidance on appropriate scenarios.
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.