Check a URL or domain for phishing and scam risk before visiting, clicking, or transacting with it. Returns a verdict (safe | warn | danger), a 0-100 risk score, a plain-English summary, and specific findings (brand impersonation, typosquatting, community scam reports, threat-feed hits). Costs 1 scan credit.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL or bare domain to analyze, e.g. "paypal-verify-login.xyz" |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses expected outputs (verdict, 0-100 risk score, plain-English summary, and specific findings) and a side-effect cost ('Costs 1 scan credit'). It does not mention rate limits or auth, but for a simple scan tool this is solid behavioral disclosure.
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?
Two sentences: the first states the purpose and returns, the second adds the cost. Every sentence earns its place, with the core purpose front-loaded and no redundant filler.
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 single-parameter tool with no output schema, the description compensates by enumerating all return fields and the credit cost. An agent can call it correctly and interpret its result without missing critical information.
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 already covers the single 'url' parameter with 100% description coverage and an example format. The description adds no additional parameter semantics, so the baseline of 3 applies.
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 opens with a specific verb and resource: 'Check a URL or domain for phishing and scam risk before visiting, clicking, or transacting with it.' It clearly distinguishes this tool from siblings (get_usage, scan_message) by focusing on URL/domain scanning, leaving no ambiguity about the target.
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 gives clear usage context: use before visiting, clicking, or transacting. It does not explicitly name alternatives or exclusion conditions, but the trigger scenarios are specific enough to guide an agent's decision without needing comparison to siblings.
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.
Each tool targets a clearly distinct purpose: checking usage, scanning message content, and scanning URLs. Even though scan_message and scan_url share the 'scan' verb, their input types and use cases are unambiguous.
All tool names follow a consistent verb_noun pattern: get_usage, scan_message, scan_url. The naming is predictable and matches the resource each operation acts on.
Three tools is a well-scoped count for a scam-detection service. Each tool is necessary for the core workflow: checking remaining credits, scanning textual content, and scanning URLs.
The server covers the primary scam-detection surfaces—free-form text/messages and URLs—along with a usage/credit check for operational awareness. There are no obvious dead ends or missing lifecycle operations given the service's focused purpose.