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jeffneo

neo4j-mcp-gateway

by jeffneo

usecase_detect_synthetic_identity

Detect synthetic identity fraud by clustering identities with shared PII elements that lack legitimate relationships, revealing suspicious patterns.

Instructions

Identifies potential synthetic identities by finding clusters of identities sharing PII elements without legitimate relationship explanation.

(Use-case tool from detect_synthetic_identity.yaml; runs curated Cypher in read mode.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return
customer_idNoSpecific customer ID to investigate (omit for discovery mode)
min_shared_attributesNoMinimum number of shared attributes to flag as suspicious
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It openly states that it runs curated Cypher in read mode, communicating a read-only operation and a pre-defined query. While it does not detail output format or edge cases, this is a useful behavioral trait beyond what the schema provides.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loads the core purpose, and includes a compact source/PII note. Every word adds value with no redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a three-parameter tool without an output schema, the description provides enough context for an agent to select and invoke it: it explains the purpose, the read-only mode, and the discovery-mode hint derived from the customer_id parameter. It does not describe return values, but the lack of an output schema and the straightforward nature of the tool make this acceptable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already explains limit, customer_id, and min_shared_attributes. The tool description adds little parameter-specific meaning beyond the conceptual notion of 'clusters sharing PII elements,' which only loosely relates to min_shared_attributes. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action (identifies potential synthetic identities) and the method (clusters of identities sharing PII elements). This distinguishes it from sibling tools like movie search and high-risk transaction detection, which are entirely different use cases.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The first sentence explicitly states the tool's purpose, making it clear when to use it: when there is a need to detect synthetic identities. The note about being a use-case tool and running curated Cypher in read mode provides additional context, though it does not explicitly name alternative tools or exclusions.

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

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