get_account
Return the current NeuroTrade B2B API quota status: plan tier, calls used, calls remaining, and quota reset date. Requires a valid NeuroTrade B2B API key.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Return the current NeuroTrade B2B API quota status: plan tier, calls used, calls remaining, and quota reset date. Requires a valid NeuroTrade B2B API key.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 actively discloses the authentication requirement (valid API key) and specifies the exact data returned. While it does not explicitly state read-only or side effects, the nature of a quota status query and the listed fields make the behavior clear enough.
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, front-loaded with the purpose. Every word adds value: the verb, the resource, the specific return fields, and the auth requirement. No fluff or redundancy.
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 zero-parameter tool with no output schema, the description is quite complete: it states the purpose, lists the returned fields, and mentions the required authentication. It could add error-handling or response format details, but those are not essential for such a straightforward query tool.
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 is empty, meaning there are zero parameters. The description does not need to explain parameters, and the baseline for 0 params is 4. The description adds value by explaining what the tool returns, but that is not parameter semantics, so no additional parameter detail is required.
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 'Return' and clearly identifies the resource: NeuroTrade B2B API quota status. It lists the specific fields returned (plan tier, calls used, calls remaining, quota reset date), which distinguishes it from the sibling tool generate_signal that likely focuses on signals, not account status.
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 implies usage when needing quota/account status and provides a required prerequisite (valid API key). However, it does not explicitly state when to prefer this over alternatives or provide exclusions, so the usage guidance is implicit rather than direct.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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The two tools serve completely different purposes: one generates trading signals, the other checks account quota. There is no ambiguity between them.
Both tools follow the verb_noun pattern: generate_signal and get_account. This is consistent and predictable.
With only 2 tools, the surface feels thin for a trading signal API, which typically might include backtesting or historical data. However, the tools are directly relevant to the core purpose.
The set covers signal generation and quota management, but lacks obvious supporting operations like fetching historical signals or managing signal subscriptions. This leaves some gaps for an agent needing broader trading workflow coverage.