get_forex_signal

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 discloses both success and error return formats, including conditions for missing features or unsupported tickers. It does not discuss side effects or permissions, but for a model inference tool these are less critical.

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 concise and well-structured: a one-sentence action summary, a short parameter list, and a clear return/error specification. Every sentence adds value with no 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?

Given the tool's modest complexity, an output schema, and two parameters, the description covers the invocation flow completely: prerequisite (get_market_features), parameter semantics, success return shape, and error conditions. This is sufficient for an agent to call the tool correctly.

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 0%, but the description compensates by specifying example ticker values and defining features as the dict returned by get_market_features. It does not enumerate all possible tickers or the internal structure of features, but it provides essential linkage to the upstream tool.

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 states a specific action: 'Run the XGBoost model' to generate a forex signal from pre-computed features. It clearly distinguishes itself from the sibling tool get_market_features by positioning itself as the consumer of those features.

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?

It explicitly states that features must come from get_market_features, establishing a clear prerequisite and usage context. It does not list when not to use the tool or alternatives, but with only one sibling the guidance is sufficient.

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