get_usage
Check the API key’s remaining prepaid scan credits, monthly plan usage, and credit expiry. Free — consumes no credits.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Check the API key’s remaining prepaid scan credits, monthly plan usage, and credit expiry. Free — consumes no credits.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 full burden of behavioral disclosure. It explicitly states a key behavioral trait: 'Free — consumes no credits,' which tells the agent this tool has no cost side-effect. It also implies read-only status through 'Check,' which covers the main transparency concern for a usage tool.
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: one sentence plus a short clause. It front-loads the purpose ('Check') and lists the key output dimensions without any filler. Every word contributes value.
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 zero-parameter, no-output-schema tool, the description is fully complete. It tells the agent exactly what data can be obtained (prepaid credits, monthly usage, expiry), confirms there is no cost, and requires no additional information for correct invocation.
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 tool has zero parameters and an empty input schema, so parameter semantics have no ambiguity. Per the baseline for 0-parameter tools, a score of 4 is appropriate; the description need not add anything about parameters.
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 ('Check') and names a precise resource: the API key's remaining prepaid scan credits, monthly plan usage, and credit expiry. This clearly distinguishes get_usage from siblings scan_message and scan_url, which perform scans rather than report usage.
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 clearly implies when to use this tool: when an agent needs to check credit/usage status. It also states it is free and consumes no credits, which is useful guidance for selecting it safely over operations that would consume credits. It does not explicitly name alternatives or exclusions, but the context is sufficient.
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.