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mcp-knowledge

by io.github.getgapup

revops_architect

Read-only

Architecte RevOps — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Qonto — ARR €200M · 200 reps · forecast ±35% · fuite €4,2M/an identifiée · plan RevOps 12 semaines. 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
keyMetricsYes
objectivesYes
revenueTeamYes
currentStackYes
horizonMonthsYes
currentPainPointsYes

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: +{
+  "currentArrEur": {
+    "type": "number"
+  },
+  "name": {
+    "type": "string"
+  },
+  "salesMotion": {
+    "enum": [
+      "inbound",
+      "outbound",
+      "plg",
+      "hybrid"
+    ],
+    "type": "string"
+  },
+  "sector": {
+    "type": "string"
+  },
+  "teamSize": {
+    "type": "number"
+  }
+}
    • addedInput schema / properties / company / required
      Added value: +[
+  "name",
+  "sector",
+  "currentArrEur",
+  "teamSize",
+  "salesMotion"
+]
    • addedInput schema / properties / currentPainPoints / items
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / currentStack / items
      Added value: +{
+  "properties": {
+    "category": {
+      "enum": [
+        "crm",
+        "marketing-automation",
+        "sdr-tool",
+        "analytics",
+        "revenue-intelligence",
+        "cs-platform",
+        "other"
+      ],
+      "type": "string"
+    },
+    "satisfaction": {
+      "enum": [
+        "poor",
+        "ok",
+        "good"
+      ],
+      "type": "string"
+    },
+    "tool": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "tool",
+    "category",
+    "satisfaction"
+  ],
+  "type": "object"
+}
    • addedInput schema / properties / keyMetrics / properties
      Added value: +{
+  "avgSalesCycleDays": {
+    "type": "number"
+  },
+  "dataQualityScore": {
+    "enum": [
+      "poor",
+      "fair",
+      "good",
+      "excellent"
+    ],
+    "type": "string"
+  },
+  "forecastAccuracyPct": {
+    "type": "number"
+  },
+  "leadToOpportunityPct": {
+    "type": "number"
+  },
+  "opportunityToClosePct": {
+    "type": "number"
+  }
+}
    • addedInput schema / properties / objectives / items
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / revenueTeam / properties
      Added value: +{
+  "aes": {
+    "type": "number"
+  },
+  "csms": {
+    "type": "number"
+  },
+  "hasRevOpsFunction": {
+    "type": "boolean"
+  },
+  "revOpsHeadcount": {
+    "type": "number"
+  },
+  "sdrs": {
+    "type": "number"
+  }
+}
    • addedInput schema / properties / revenueTeam / required
      Added value: +[
+  "sdrs",
+  "aes",
+  "csms",
+  "hasRevOpsFunction"
+]
    • addedInput schema / required
      Added value: +[
+  "company",
+  "revenueTeam",
+  "currentStack",
+  "currentPainPoints",
+  "keyMetrics",
+  "objectives",
+  "horizonMonths"
+]
  2. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already indicate read-only and open-world behavior. The description adds that inputs are validated server-side, but no additional behavioral traits beyond what annotations provide.

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 short (3 sentences) and front-loaded with purpose. It is efficient but uses French, which may not be ideal for all users.

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

Completeness3/5

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

Given no output schema and 8 parameters with low coverage, the description is somewhat incomplete. It lacks details on return format and parameter semantics, though the general purpose is clear.

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 coverage is only 13%, and the description does not compensate. It says 'send the documented case fields' but provides no explanation of parameters despite having 8 complex parameters with nested objects.

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

Purpose4/5

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

The description clearly states it is an 'Architecte RevOps' that returns a structured, audited deliverable, with a reference case. It distinguishes itself from sibling tools by specifying CRO expertise and a structured output.

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

Usage Guidelines3/5

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

The description implies use for RevOps architecture but does not explicitly state when to use over alternatives. No when-not guidance or alternative tool names are provided, though the reference case hints at context.

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