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mcp-knowledge

by io.github.getgapup

margin_doctor_finance

Read-only

Médecin des Marges — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Reference case: Alan — ARR €60M · marge brute 68% → 79% · €3,2M fuites identifiées · Rule of 40 : 14→38. 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.
companyYes
costBreakdownYes
marginTargetsYes
unitEconomicsYes
incomeStatementYes

Schema Changelog

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

  1. Changed9 schema fields changed
    • addedInput schema / properties / company / properties
      Added value: +{
+  "businessModel": {
+    "enum": [
+      "saas",
+      "marketplace",
+      "services",
+      "hybrid"
+    ],
+    "type": "string"
+  },
+  "currentArrEur": {
+    "type": "number"
+  },
+  "name": {
+    "type": "string"
+  },
+  "revenueLastYearEur": {
+    "type": "number"
+  },
+  "sector": {
+    "type": "string"
+  },
+  "teamSize": {
+    "type": "number"
+  }
+}
    • addedInput schema / properties / company / required
      Added value: +[
+  "name",
+  "sector",
+  "currentArrEur",
+  "revenueLastYearEur",
+  "teamSize",
+  "businessModel"
+]
    • addedInput schema / properties / costBreakdown / items
      Added value: +{
+  "properties": {
+    "amountEur": {
+      "type": "number"
+    },
+    "category": {
+      "type": "string"
+    },
+    "controllable": {
+      "type": "boolean"
+    },
+    "percentOfRevenue": {
+      "type": "number"
+    },
+    "trend": {
+      "enum": [
+        "growing",
+        "stable",
+        "declining"
+      ],
+      "type": "string"
+    }
+  },
+  "required": [
+    "category",
+    "amountEur",
+    "percentOfRevenue",
+    "trend",
+    "controllable"
+  ],
+  "type": "object"
+}
    • addedInput schema / properties / incomeStatement / properties
      Added value: +{
+  "cogsEur": {
+    "type": "number"
+  },
+  "ebitdaEur": {
+    "type": "number"
+  },
+  "ebitdaMarginPct": {
+    "type": "number"
+  },
+  "generalAndAdminEur": {
+    "type": "number"
+  },
+  "grossMarginPct": {
+    "type": "number"
+  },
+  "grossRevenueEur": {
+    "type": "number"
+  },
+  "researchAndDevEur": {
+    "type": "number"
+  },
+  "salesAndMarketingEur": {
+    "type": "number"
+  }
+}
    • addedInput schema / properties / incomeStatement / required
      Added value: +[
+  "grossRevenueEur",
+  "cogsEur",
+  "grossMarginPct",
+  "researchAndDevEur",
+  "salesAndMarketingEur",
+  "generalAndAdminEur",
+  "ebitdaEur",
+  "ebitdaMarginPct"
+]
    • addedInput schema / properties / marginTargets / properties
      Added value: +{
+  "targetEbitdaMarginPct": {
+    "type": "number"
+  },
+  "targetGrossMarginPct": {
+    "type": "number"
+  },
+  "targetTimeframeMonths": {
+    "type": "number"
+  }
+}
    • addedInput schema / properties / marginTargets / required
      Added value: +[
+  "targetGrossMarginPct",
+  "targetEbitdaMarginPct",
+  "targetTimeframeMonths"
+]
    • addedInput schema / properties / unitEconomics / properties
      Added value: +{
+  "avgContractValueEur": {
+    "type": "number"
+  },
+  "cacEur": {
+    "type": "number"
+  },
+  "grossMarginPerCustomerEur": {
+    "type": "number"
+  },
+  "ltvEur": {
+    "type": "number"
+  },
+  "paybackMonths": {
+    "type": "number"
+  }
+}
    • addedInput schema / required
      Added value: +[
+  "company",
+  "incomeStatement",
+  "costBreakdown",
+  "unitEconomics",
+  "marginTargets"
+]
  2. First observed

TDQS

C2.6/5.0
Behavior3/5

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

Annotations provide readOnlyHint=true, which is consistent with returning a deliverable. The description adds that inputs are validated server-side but does not disclose behavioral traits like cost or rate limits. No contradiction, but the description could be more informative.

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 but includes non-English phrases and a reference case that may be extraneous. It front-loads the French label and jargon rather than a clear, direct statement of purpose.

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 (6 parameters, nested objects, no output schema), the description fails to explain the return deliverable's structure or the tool's analysis scope. The reference case provides some context but is insufficient for complete understanding.

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 17%, meaning most properties lack descriptions. The description does not elaborate on parameter meanings or required fields, leaving the agent to rely on the sparse schema. 'Send the documented case fields' is too vague.

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 mentions 'Médecin des Marges' and 'Returns a structured, audited deliverable', indicating it analyzes margins and produces a report, but the exact verb and resource are unclear due to jargon and French phrasing. The reference case hints at margin improvement but lacks a specific action statement.

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?

There is no guidance on when to use this tool versus alternatives like 'margin_doctor'. The description only states 'send the documented case fields' without specifying context, prerequisites, 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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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