Score fraud risk for every entity in a graph by propagating analyst seeds and model priors across relationships. Falls back to structural scoring when the model is unavailable.
Score fraud risk for every entity in a graph by propagating analyst seeds and model priors across relationships. Degrades to a deterministic structural fallback when the model is unavailable.
| Name | Required | Description | Default |
|---|---|---|---|
| graph | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explains the core computation and the fallback behavior when the model is unavailable, but it does not disclose side effects, read-only semantics, error behavior, or expected outputs. With no annotations, more behavioral detail would be beneficial.
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, using two straightforward sentences. It front-loads the primary purpose and follows with the fallback behavior, with no redundant or extraneous information.
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?
The complex nested graph schema with node kinds, seeds, edge weights, and attributes is not elaborated. There is also no output schema or description of return values, making the description incomplete for fully understanding how to construct inputs or interpret results.
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%, and the description does not explain individual parameters such as 'seed', 'attributes', 'weight', or 'relation'. It provides general context about graph relationships but leaves parameter meaning largely to inference.
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 fraud risk', the resource 'every entity in a graph', and the method 'propagating analyst seeds and model priors across relationships'. It also mentions the deterministic fallback, making the tool's intent unmistakable.
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 provides clear context for when to use the tool: whenever fraud risk scores are needed for entities in a graph. There are no explicit exclusions, but no sibling tools exist, so the absence of explicit alternative guidance is acceptable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.