get_accountRead the account: the plan, this month's usage and the plan's limits on contacts, monthly sends and projects. Check it before a large import or broadcast.
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "get_account"
}
}| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
While annotations confirm it is read-only, the description adds valuable operational context by detailing the specific limits being monitored (contacts, sends, projects). It tells the agent *why* this tool is useful, which is a key behavioral insight beyond the metadata.
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 definition is concise, using only two sentences to convey the full purpose, content, and optimal usage scenario. There is no wasted language.
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 that the tool has no parameters and relies on the agent understanding the platform's operational limits, this description is perfectly complete. It tells the agent everything it needs to know before and during the call.
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?
Since the input schema contains zero parameters, the description cannot add parameter semantics. However, it provides critical context on the overall account structure that is otherwise unknown to the agent, earning a high score for compensating for the lack of structural data.
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 identifies the resource (the account) and specifies the nature of the action (read), detailing exactly what data points are included (plan, usage, limits). This specific listing helps distinguish it from broader overview tools in the siblings.
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 usage guidance is extremely explicit: 'Check it before a large import or broadcast.' This provides a clear operational trigger for when an agent should invoke this tool.
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.