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fraud_detector

Read-only

Détecteur de fraude — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Reference case: TechManu SAS — Industriel FR €32M CA, 148 FTE · 30j · 21 anomalies · €487k à risque. Inputs are validated server-side — send the documented case fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
focusNo
companyYes
analysisPeriodDaysYes
transactionVolumesYes

TDQS

C2.4/5.0
Behavior2/5

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

Annotations declare readOnlyHint=true, so the tool is read-only. The description adds 'Returns a structured, audited deliverable' and mentions server-side validation, but does not disclose execution time, side effects, or authentication requirements. With annotations already covering read-only behavior, the description adds minimal value.

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

Conciseness3/5

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

The description is relatively short (3 sentences) but includes an unhelpful reference case and begins in French, which may not be appropriate for an international agent. The structure front-loads purpose but could be more concise and English-only.

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

Completeness2/5

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

Given the tool's complexity (5 parameters, nested objects, no output schema), the description is incomplete. It does not explain how to construct inputs, what the output contains, or how the tool integrates with other tools. The reference case provides a partial example but is not systematic.

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

Parameters2/5

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

Schema description coverage is only 20% (only 'async' parameter has a description). The description includes a reference case that hints at parameter values (company name, sector, revenue, etc.), but does not systematically explain each parameter or nested object fields. This is insufficient for a tool with 5 parameters and nested objects.

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

Purpose3/5

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

The name 'fraud_detector' clearly indicates fraud detection, and the description mentions 'Détecteur de fraude' and returning a structured deliverable. However, the description is in French and includes jargon ('Gapup agent-payable C-suite expertise') that may confuse, and it does not explicitly state the tool's scope or output format. Purpose is adequate but vague.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. Sibling tools include similar fraud detection tools (e.g., 'affiliate_fraud_clickstream_detector', 'x402_payment_fraud_detector'), but no differentiators or usage contexts are given.

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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TDQS

C2.5/5.0
Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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