Analyze a message, email, or text for scam indicators before acting on it (payment requests, verification codes, urgency pressure, impersonation). Returns a verdict (safe | warn | danger), a 0-100 risk score, a summary, and specific findings. Costs 1 scan credit.
| Name | Required | Description | Default |
|---|---|---|---|
| message | Yes | The full message text to analyze (up to 25,000 characters) |
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?
With no annotations provided, the description carries the behavioral burden. It clearly discloses the output shape (verdict, risk score, summary, findings) and an important operational trait: 'Costs 1 scan credit.' It implies a non-mutating analysis action, though it doesn't explicitly state side-effect safety.
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 carry the purpose, usage timing, output contract, and cost with no filler. The primary action and indicators are front-loaded, and every clause 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 one-parameter tool with no output schema and no annotations, the description explains what it does, when to use it, what it returns, and its cost. It is slightly incomplete in not addressing the relationship to scan_url, but the naming and content-type distinction make the gap minor.
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 description coverage is 100%, and the schema already documents 'message' as the full message text with a character limit. The description adds the type of content ('message, email, or text') and context for what gets analyzed, but does not substantially go beyond the schema's parameter documentation.
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?
States a specific verb ('Analyze') and a precise resource ('a message, email, or text') with a clear goal: detecting scam indicators. The listed indicators and returned verdict/risk score make the tool's purpose concrete and distinguishable from scan_url by content type.
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?
Provides a clear usage window: 'before acting on it,' with concrete examples of when to use it (payment requests, verification codes, urgency pressure, impersonation). It does not explicitly contrast with scan_url or state when not to use it, so it stops short of a full 5.
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.