NeuroTrade Signal API

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full weight. It discloses authentication requirements, error behavior (QUOTA_EXCEEDED with Retry-After), the conditional presence of the thesis field, and a full list of returned fields. This goes beyond minimal transparency, though it does not explicitly mention side effects or read-only status.

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 a single, dense sentence that front-loads the purpose and uses a colon-delimited list for the return payload. It packs substantial information without excess fluff, though its length is somewhat longer than the minimum needed.

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 the lack of an output schema and annotations, the description provides a comprehensive picture: all return fields, optional field behavior, error handling, and authentication. This is sufficient for an agent to invoke the tool correctly with no ambiguity.

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?

All parameters have complete schema descriptions, so the baseline is 3. The description adds valuable context beyond the schema: it explains that include_thesis introduces latency and controls the thesis field, and it highlights symbol and timeframe as core inputs. This extra behavioral detail justifies a 4.

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 tool's action ('Generate an AI-powered crypto trading signal') and its key constraints (given pair and timeframe). It distinguishes the tool from the sibling get_account by focusing on signal generation, and it enumerates expected return fields, making the purpose explicit.

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 the tool is for generating signals but does not explicitly state when to use it versus other tools. There is no mention of alternatives or exclusions, so the usage guidance is only implied, not directly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.