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fraud-mcp

fraud-mcp

by Rajkumar2002-Rk
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Look up device history

lookup_device_history
Read-onlyIdempotent

Shows every account and login event for a device to reveal connected accounts and high-risk account-takeover activity.

Instructions

Return every account and login event seen on a device.

Use this to pivot from one compromised account to the others driven from the
same machine - the standard way an account-takeover ring is mapped. Pay
attention to `distinct_accounts_with_events`, `failed_login_count`, and
`password_reset_count`: a device with many accounts, failed logins, and resets
is a takeover tool, not a shared family tablet.

Returns an error envelope for unknown or malformed device ids.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "device_id": "<device_id>"
    },
    "name": "lookup_device_history"
  }
}

Input Schema

NameRequiredDescriptionDefault
device_idYesDevice identifier such as 'DEV-2000'. Obtain it from the `device_id` field of a transaction, or from `evidence_device_ids` on a SHARED_DEVICE rule result. Device ids are NOT derived from account ids - 'DEV-1013' is not the device for 'ACC-1013'.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, covering safety. The description adds the error envelope for unknown or malformed IDs, which is behavioral information not present in annotations. It also hints at the nature of the output (account and login events) without contradicting any hints. This adds value beyond the structured metadata.

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

Conciseness4/5

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

The description is about 80 words, covering purpose, usage, interpretation, and error behavior in four sentences. It is front-loaded with the purpose statement and avoids redundancy with schema fields. The structure is logical and every sentence earns its place, though it could be slightly shorter without losing value.

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 single-parameter read-only tool with an output schema present, the description is nearly complete. It covers the main purpose, usage scenario, error handling, and even hints at result fields. It does not explicitly mention prerequisites or permissions, but the read-only annotation and error message cover most operational needs. Minor gaps like pagination or limits are not critical for this scope.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds meaning by explaining where to obtain device_id (from transaction device_id field or evidence_device_ids) and explicitly clarifying that device IDs are not derived from account IDs, which prevents a common mistake. This goes beyond the schema's pattern and examples, making it a 4.

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 opens with a specific verb+resource: 'Return every account and login event seen on a device.' It clearly states scope (all accounts and logins) and distinguishes its investigative role from siblings by naming the pivot use case (mapping an account-takeover ring). This is unambiguous and sets it apart from get_transactions, evaluate_fraud_rules, and flag_case without opening schemas.

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?

It gives explicit context for when to use this tool: 'Use this to pivot from one compromised account to the others driven from the same machine.' It also provides interpretive guidance on which fields to focus on, implying when results are suspicious. It does not explicitly name alternatives or state when not to use it, but the context is strong enough for an agent to select it appropriately.

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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