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Glama

mcp-knowledge

by io.github.getgapup

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

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed9 schema fields changed
    • addedInput schema / properties / analysisPeriodDays / maximum
      Added value: +365
    • addedInput schema / properties / analysisPeriodDays / minimum
      Added value: +7
    • changedInput schema / properties / analysisPeriodDays / type
      Previous value: -"number"New value: +"integer"
    • addedInput schema / properties / company / properties
      Added value: +{
+  "country": {
+    "maxLength": 80,
+    "minLength": 2,
+    "type": "string"
+  },
+  "fte": {
+    "minimum": 1,
+    "type": "integer"
+  },
+  "name": {
+    "maxLength": 120,
+    "minLength": 2,
+    "type": "string"
+  },
+  "revenueEur": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "sector": {
+    "maxLength": 120,
+    "minLength": 2,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / company / required
      Added value: +[
+  "name",
+  "sector",
+  "fte",
+  "revenueEur",
+  "country"
+]
    • addedInput schema / properties / focus / maxLength
      Added value: +500
    • addedInput schema / properties / transactionVolumes / properties
      Added value: +{
+  "bankTransactionsPerMonth": {
+    "minimum": 0,
+    "type": "integer"
+  },
+  "expenseReportsPerMonth": {
+    "minimum": 0,
+    "type": "integer"
+  },
+  "supplierInvoicesPerMonth": {
+    "minimum": 0,
+    "type": "integer"
+  },
+  "totalAmountEur": {
+    "minimum": 0,
+    "type": "number"
+  }
+}
    • addedInput schema / properties / transactionVolumes / required
      Added value: +[
+  "bankTransactionsPerMonth",
+  "supplierInvoicesPerMonth",
+  "expenseReportsPerMonth",
+  "totalAmountEur"
+]
    • addedInput schema / required
      Added value: +[
+  "company",
+  "analysisPeriodDays",
+  "transactionVolumes"
+]
  2. First observed

TDQS

C2.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, so the read-only nature is clear. The description adds that it returns a structured, audited deliverable and validates inputs server-side, which is consistent and provides some behavioral context without contradiction.

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

Conciseness2/5

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

The description is verbose with marketing language ('Gapup agent-payable C-suite expertise (RISK)') and a reference case that adds little value for tool selection. It could be more concise and focused on essential information.

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 complex nested input schema and lack of output schema, the description fails to explain the return format, authentication needs, or how the async parameter works. It only vaguely mentions a 'structured, audited deliverable', which is insufficient.

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

Parameters1/5

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

Schema description coverage is only 20%, and the description provides no explanation of the parameters. It only mentions 'send the documented case fields' without detailing what fields are needed, leaving the agent to rely solely on the schema.

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 description states it is a fraud detector and returns a structured deliverable, but does not clearly specify what type of fraud (e.g., financial, transactional) nor distinguish it from similar siblings like x402_payment_fraud_detector. The purpose is somewhat clear but lacks specificity.

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?

No guidance on when to use this tool versus alternatives. It only mentions that inputs are validated server-side, but does not provide context for selection or exclusion of other tools.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources