check_fraud_risk

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 discloses that the data is real and not mock ('100% Real Live', 'Zero mock data'), which is valuable for setting expectations. However, it does not explicitly state that the operation is read-only, mention any rate limits, authentication requirements, or potential side effects. The 'returns' wording implies a read operation, but it is not explicitly declared. This is a moderate gap given no annotations exist.

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 sentence that front-loads the core purpose ('100% Real Live nationwide courier fraud check') and then lists the outputs. Every phrase serves a purpose: it clarifies the scope (nationwide Bangladesh, Steadfast API), the data quality (real, not mock), and the results (counts, success rate, risk evaluation). No wasted words.

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 tool with one parameter and no output schema, the description covers the essential points: it states the operation (fraud check), the input (a phone number), the output (metrics and risk evaluation), and the data authenticity. It does not mention error handling or interpretation of the risk evaluation, but these are not strictly necessary for calling the tool correctly. The description is sufficient for an agent to understand what the tool does and what it returns.

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 schema description for 'phone' is complete (100% coverage), stating it expects an 11-digit Bangladeshi mobile number. The tool description adds minimal extra meaning—only that the phone is used for the fraud check. It does not explain how the number is used beyond 'evaluate,' but since the schema already documents the parameter's format and purpose, a baseline of 3 is appropriate. The description does not conflict or add significant depth.

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 states the tool's purpose: a nationwide courier fraud check for Bangladesh logistics networks. It names the specific data returned (delivery/cancellation counts, success rate, risk evaluation) and distinguishes itself from siblings like track_parcel or get_balance by focusing on fraud assessment. The verb 'check' and resource 'fraud risk' are 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 used to evaluate a phone number for fraud risk, but it does not explicitly state when to use it over alternatives or provide exclusions. It lacks guidance like 'use this before shipping to a new number' or 'use track_parcel for existing orders.' The context is clear but not explicitly framed against siblings.

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