security_typosquatFlags homoglyph and edit-distance look-alikes of known brands (c0inbase, b1nance, etc.). $0.005 per call.
| Name | Required | Description | Default |
|---|---|---|---|
| brands | No | Optional comma-separated brand list to check against (defaults to a built-in set) | |
| domain | Yes | Domain to analyse for brand impersonation (e.g. c0inbase.com) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context beyond these by disclosing the cost ($0.005 per call) and the algorithmic approach (homoglyph and edit-distance matching). This helps the agent understand the tool's cost profile and how it flags look-alikes.
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 two concise sentences with no filler. The primary function is front-loaded, and the pricing note is included as a separate short sentence. Every word earns its place.
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?
For a simple two-parameter tool with one required parameter, full schema coverage, and safety annotations, the description covers purpose, method, cost, and parameter roles. The only gap is that it does not describe the return format, but with no output schema required, this is a minor omission in an otherwise complete definition.
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% because both 'domain' and 'brands' have clear descriptions. The description's mention of 'known brands' and the built-in set aligns with the brands parameter but adds no new syntax, defaults, or format beyond what the schema already provides. Thus it meets the baseline but does not exceed it.
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 uses a specific verb 'Flags' with a clearly defined resource: homoglyph and edit-distance look-alikes of known brands. The examples (c0inbase, b1nance) and the title 'Typosquat / look-alike domain check' make the tool's purpose unambiguous and distinguish it from siblings like security_email and security_tls.
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?
No explicit guidance is provided on when to use this tool versus alternatives. The description implies it is for checking whether a domain impersonates a brand, but it does not state when to prefer it over other security tools or when not to use it. This leaves the agent to infer the appropriate context.
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.
Most tools are cleanly separated by domain prefix (data_, security_, validate_, verify_) and describe distinct outputs. A couple of pairs like token_verdict vs verify_token or data_portfolio vs wallet_dossier could be confused until descriptions are read, but the descriptions resolve the ambiguity.
The majority of tools follow a predictable prefix_subject pattern, e.g. data_block, security_tls, validate_iban, verify_payment. A few outliers like html_to_markdown, text_diff, wallet_dossier, and token_verdict use different conventions, but the overall system remains readable.
20 tools is on the heavy side for the typical MCP server and sits in the 16–25 'feels heavy' zone. Most tools have a legitimate purpose, but the five validate_* identifier tools plus two generic utilities could feel like surface area bloat.
The server covers a broad and coherent read/validation domain: chain data, token safety, payments, security checks, and identifier validators. Minor gaps exist, such as lack of detailed historical transaction/activity data and no general network/domain security scan beyond email, TLS, and typosquat.