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Glama

mcp-knowledge

by io.github.getgapup

margin_doctor

Read-only

Marge par deal — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Gapup Hub — 8 deals pipeline · €28k ARR sous-marge détecté · Récupération €4.2k/an · Playbook 4 scénarios. 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.
dealsYes
companyYes
productYes

Schema Changelog

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

  1. Changed8 schema fields changed
    • addedInput schema / properties / company / properties
      Added value: +{
+  "name": {
+    "maxLength": 200,
+    "minLength": 2,
+    "type": "string"
+  },
+  "sector": {
+    "maxLength": 100,
+    "minLength": 2,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / company / required
      Added value: +[
+  "name",
+  "sector"
+]
    • addedInput schema / properties / deals / items
      Added value: +{
+  "properties": {
+    "arrEur": {
+      "minimum": 0,
+      "type": "number"
+    },
+    "closingDate": {
+      "type": "string"
+    },
+    "cogsEur": {
+      "minimum": 0,
+      "type": "number"
+    },
+    "competitors": {
+      "items": {
+        "maxLength": 100,
+        "type": "string"
+      },
+      "maxItems": 3,
+      "minItems": 0,
+      "type": "array"
+    },
+    "discountPct": {
+      "maximum": 100,
+      "minimum": 0,
+      "type": "number"
+    },
+    "id": {
+      "maxLength": 50,
+      "minLength": 1,
+      "type": "string"
+    },
+    "name": {
+      "maxLength": 200,
+      "minLength": 2,
+      "type": "string"
+    },
+    "urgency": {
+      "enum": [
+        "high",
+        "medium",
+        "low"
+      ],
+      "type": "string"
+    }
+  },
+  "required": [
+    "id",
+    "name",
+    "arrEur",
+    "discountPct"
+  ],
+  "type": "object"
+}
    • addedInput schema / properties / deals / maxItems
      Added value: +15
    • addedInput schema / properties / deals / minItems
      Added value: +2
    • addedInput schema / properties / product / properties
      Added value: +{
+  "name": {
+    "maxLength": 200,
+    "minLength": 2,
+    "type": "string"
+  },
+  "targetGrossMarginPct": {
+    "maximum": 100,
+    "minimum": 0,
+    "type": "number"
+  }
+}
    • addedInput schema / properties / product / required
      Added value: +[
+  "name",
+  "targetGrossMarginPct"
+]
    • addedInput schema / required
      Added value: +[
+  "company",
+  "product",
+  "deals"
+]
  2. First observed

TDQS

C2.8/5.0
Behavior4/5

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

Annotations already provide readOnlyHint=true and openWorldHint=true. The description adds that inputs are validated server-side and returns an audited deliverable, reinforcing the safe, read-only nature without contradictions.

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 a detailed case reference that may not be essential. Could be more concise and front-loaded with actionable 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?

The tool has nested objects and no output schema. The description mentions a 'structured, audited deliverable' but does not specify its contents or format, leaving gaps for an agent to understand the full context.

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 25% (only 'async' has a description). The description does not elaborate on 'company', 'product', or 'deals' parameters, leaving the agent without added meaning beyond the schema structure.

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's about 'Marge par deal' and returns a structured, audited deliverable, but uses French terms and references a specific case, making the exact purpose unclear. It does not distinguish from the sibling 'margin_doctor_finance'.

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. The description only says 'send the documented case fields', lacking context for when it is appropriate or when to avoid it.

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