Username OSINT report. Checks 25+ platforms (GitHub, Reddit, Twitter, Instagram, npm, Steam, etc.) for a username. Categorizes by type, enriches GitHub profile, checks domain availability.
| Name | Required | Description | Default |
|---|---|---|---|
| username | Yes | Username to investigate |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for disclosing behavior. It effectively communicates that the tool performs multiple actions (checks platforms, categorizes, enriches GitHub, checks domains) and implies a non-destructive, read-only report generation. It does not mention potential limitations like rate limits or failure modes, but given the tool's nature, the key behaviors are transparently outlined.
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 extremely concise—two short sentences that front-load the tool's identity ('Username OSINT report') and immediately enumerate specific capabilities. Every word adds value, with no redundancy or filler. It efficiently conveys the full scope of the tool without unnecessary elaboration.
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 complexity (scanning 25+ platforms, enrichment, domain check), the description provides a solid overview of what to expect. Since there is no output schema, it appropriately indicates that a 'report' is generated, though it doesn't specify the report structure or content. It also doesn't mention any prerequisites or edge cases, but for a single-parameter tool, the description is reasonably complete and sets correct expectations about the tool's multi-step behavior.
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 input schema fully documents the sole parameter 'username' with a simple description ('Username to investigate'). Since schema description coverage is 100%, the description adds no additional semantic value beyond what the schema already provides. The baseline score of 3 is appropriate because the schema handles parameter documentation completely.
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 purpose: it performs OSINT on a username by checking 25+ specific platforms, categorizing results, enriching GitHub profiles, and checking domain availability. This is a specific verb ('checks') with a clear resource (username) and distinct capabilities that differentiate it from sibling tools like domain_enrich or web_scrape, which focus on different objectives.
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 context (investigating a username across multiple platforms) but does not explicitly state when to choose this tool over alternatives or provide exclusions. It lacks direct guidance such as 'use when you need a comprehensive username footprint' or 'instead of web_scrape for username-specific data.' The implied usage is clear enough, but no explicit comparisons are made.
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.