Real-time cryptocurrency market data: price, 24h volume, market cap, and 24h price change percentage. Supports thousands of coins via CoinGecko IDs.
| Name | Required | Description | Default |
|---|---|---|---|
| coin | Yes | CoinGecko coin ID (e.g., bitcoin, ethereum, solana) | |
| currency | No | Fiat currency code (default: usd) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the responsibility for disclosing behavior. It indicates real-time data retrieval but does not explicitly state it is read-only, error handling for invalid IDs, rate limits, or any side effects. The mention of 'supports thousands of coins' gives some input expectations, but overall transparency is moderate.
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 and front-loaded with the core purpose. It consists of two short sentences with no unnecessary words or details, effectively conveying the tool's function and input scope.
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 lack of an output schema, the description adequately hints at what the tool returns by listing specific data points (price, volume, market cap, change). It is complete enough for a simple retrieval tool, though it could benefit from specifying response format or default behavior for optional parameters, which the schema already partially covers.
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 parameter semantics are already well-defined in the schema (coin as CoinGecko ID, currency with default 'usd'). The description adds minimal extra meaning, only reaffirming that coin IDs are used. It does not introduce new details about parameter behavior beyond the schema.
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 provides real-time cryptocurrency market data including price, volume, market cap, and price change. It specifies the data source (CoinGecko IDs) and scope (thousands of coins), making the purpose unambiguous.
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 does not explicitly mention when to use this tool compared to alternatives. It implies usage for current market data but lacks guidance on scenarios like historical queries or comparisons with sibling tools. Sibling tool names are present in context, but the description itself doesn't reference them.
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.