maxmind-minfraud

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

The annotations already declare readOnlyHint=true, so the description does not need to repeat that it is a read operation. The description adds useful context that the tool returns risk_score_reasons and per-factor subscores, and that the request body is identical to minfraud_score, which aids in understanding the request without contradicting annotations.

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 three sentences with no wasted words. It front-loads the core purpose, then mentions the request body similarity, and ends with the API endpoint. Every sentence adds value and the structure is efficient.

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 complexity (11 parameters, nested objects, no output schema, low schema coverage), the description lacks completeness. It does not describe the response format, the implications of omitting parameters, or how to effectively use the tool. The missing output schema and low coverage increase the burden on the description, which it does not meet.

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?

With only 18% schema description coverage, the description should compensate by explaining key parameters, but it only states 'Same request body as minfraud_score.' This does not provide enough semantic information for the 11 parameters, many of which lack descriptions in the schema.

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 title and description clearly state the tool provides the most detailed assessment including risk reasons and subscores, and it distinguishes itself from siblings by mentioning it contains everything in Insights plus additional details. The reference to the API endpoint and same request body as minfraud_score further clarifies its purpose.

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 should be used when you need risk reasons and subscores beyond Insights, but it does not explicitly state when to use it versus the sibling tools (minfraud_insights, minfraud_score, etc.). However, the comparison to Insights provides enough context for an AI agent to infer the appropriate use case.

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?

Annotations already declare readOnlyHint=true. The description adds that this is a read-only insight operation (no state change) and provides the API endpoint. No contradictions, but no additional behavioral details like authentication or rate limits.

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?

Description is three sentences, front-loaded with purpose. No fluff, but could include a summary of key outputs without being verbose.

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?

Despite complex nested parameters (11+ objects) and no output schema, the description offers no explanation of the response structure or how to interpret risk insights. This leaves the agent underinformed.

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 coverage is only 18%, so the description must compensate, but it only vaguely mentions 'IP geolocation, email/address/device attributes' without mapping to schema properties. Many parameters remain undocumented in both description and schema.

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?

Description clearly states the tool 'scores a transaction' and 'gets detailed risk insights', distinguishing it from minfraud_score by mentioning per-component risk. The verb+resource structure is specific and unambiguous.

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?

Description explicitly notes 'same request body as minfraud_score', helping the agent choose between score and insights. It implies use when detailed insights are needed, but does not explicitly exclude other siblings like minfraud_factors.

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?

The description reinforces the destructiveHint annotation by stating 'mutates MaxMind's model' and 'not a read'. It also specifies the API endpoint and HTTP 204 response, providing context beyond annotations.

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?

Four sentences, front-loaded with 'WRITE' and a clear purpose. Every sentence contributes essential information without 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 mutation tool without output schema, the description covers input requirements, side effects, and API details comprehensively. No gaps remain.

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?

With 100% schema coverage, the description still adds value by summarizing required identifiers and clarifying chargeback_code usage. It explains parameter relationships not obvious from schema alone.

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 explicitly says 'submits fraud feedback' and 'Report the real-world outcome', clearly distinguishing this write operation from sibling tools (which are read/analysis). The verb+resource combination is specific and unambiguous.

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 states the tool is for reporting real-world outcomes and labels it 'WRITE', implying it should not be used for scoring/insights. However, it lacks explicit when-not-to-use or alternative mentions.

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?

Annotations already mark the tool as readOnlyHint=true, so the description does not need to disclose mutation—but it adds value by listing returned data fields. However, it does not mention behavioral traits like rate limits or authentication requirements, though the API endpoint is provided.

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?

Two well-structured sentences: the first covers purpose and output, the second covers usage requirements. All information is front-loaded and no superfluous detail is present.

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 complexity (11 parameters, nested objects, no output schema), the description describes the main return fields and gives a usage tip, but lacks details on error cases, auth, or comprehensive return structure. It meets the minimum but could be more complete.

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?

With only 18% schema coverage, the description must compensate. It highlights the most critical parameter (device.ip_address) and suggests additional useful fields (email, billing, order). While this aids understanding, it does not explain the majority of the 11 parameters, leaving a gap.

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 verb 'score' and the resource 'transaction for fraud risk', enumerating key output fields (risk_score, IP risk, disposition, remaining query funds). It also hints at tool differentiation by calling it 'the lightest, cheapest assessment', which sets it apart from siblings that likely provide more detail.

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 provides a minimal input requirement ('Send at least device.ip_address') and a suggestion for better scoring ('plus email/billing/order'), but does not explicitly state when to use this tool versus the sibling tools (minfraud_factors, minfraud_insights, minfraud_report_transaction). Usage guidance is implied but not explicit.

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