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mcp-knowledge

by io.github.getgapup

sales_enablement_architect

Read-only

Architecte Sales Enablement — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Spendesk — 45 reps · attainment 67% · ramp 5 mois → 3 mois · programme 8 modules · +€2,1M ARR. Inputs are validated server-side — send the documented case fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gapsYes
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
salesTeamYes
objectivesYes
currentEnablementYes

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: +{
+  "averageDealSizeEur": {
+    "type": "number"
+  },
+  "name": {
+    "type": "string"
+  },
+  "productName": {
+    "type": "string"
+  },
+  "salesCycleMonths": {
+    "type": "number"
+  },
+  "sector": {
+    "type": "string"
+  },
+  "teamSize": {
+    "type": "number"
+  }
+}
    • addedInput schema / properties / company / required
      Added value: +[
+  "name",
+  "sector",
+  "productName",
+  "averageDealSizeEur",
+  "salesCycleMonths",
+  "teamSize"
+]
    • addedInput schema / properties / currentEnablement / properties
      Added value: +{
+  "battlecardsExist": {
+    "type": "boolean"
+  },
+  "contentLibraryExists": {
+    "type": "boolean"
+  },
+  "crmName": {
+    "type": "string"
+  },
+  "hasCrm": {
+    "type": "boolean"
+  },
+  "hasOnboarding": {
+    "type": "boolean"
+  },
+  "hasPlaybook": {
+    "type": "boolean"
+  },
+  "onboardingDurationWeeks": {
+    "type": "number"
+  },
+  "trainingFrequency": {
+    "enum": [
+      "never",
+      "quarterly",
+      "monthly",
+      "weekly"
+    ],
+    "type": "string"
+  }
+}
    • addedInput schema / properties / currentEnablement / required
      Added value: +[
+  "hasPlaybook",
+  "hasCrm",
+  "hasOnboarding",
+  "trainingFrequency",
+  "contentLibraryExists",
+  "battlecardsExist"
+]
    • addedInput schema / properties / gaps / items
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / objectives / items
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / salesTeam / properties
      Added value: +{
+  "aes": {
+    "type": "number"
+  },
+  "avgQuotaAttainmentPct": {
+    "type": "number"
+  },
+  "avgRampMonths": {
+    "type": "number"
+  },
+  "avgTimeOnAdminPct": {
+    "type": "number"
+  },
+  "bottomPerformerPct": {
+    "type": "number"
+  },
+  "sdrs": {
+    "type": "number"
+  },
+  "topPerformerPct": {
+    "type": "number"
+  },
+  "totalReps": {
+    "type": "number"
+  }
+}
    • addedInput schema / properties / salesTeam / required
      Added value: +[
+  "totalReps",
+  "sdrs",
+  "aes",
+  "avgRampMonths",
+  "avgQuotaAttainmentPct",
+  "avgTimeOnAdminPct",
+  "topPerformerPct",
+  "bottomPerformerPct"
+]
    • addedInput schema / required
      Added value: +[
+  "company",
+  "salesTeam",
+  "currentEnablement",
+  "gaps",
+  "objectives"
+]
  2. First observed

TDQS

B3.1/5.0
Behavior4/5

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

Beyond the readOnlyHint annotation, the description adds that the tool returns a structured audited deliverable and that inputs are validated server-side. This provides useful behavioral context without contradicting annotations.

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 relatively concise and front-loaded with the tool's purpose and a concrete example. The reference case adds value without excessive length.

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 (nested objects, no output schema), the description is incomplete. It does not specify the deliverable's structure or how to interpret results, leaving significant gaps in 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 17%, and the description does not explain individual parameters beyond a generic 'send the documented case fields.' It adds minimal value to the schema's own documentation.

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 identifies the tool as a sales enablement architect that returns a structured deliverable, with a reference case demonstrating its function. It distinguishes from sibling tools like revops_architect by focusing on CRO-level expertise, though not explicitly differentiating.

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 lacks explicit guidance on when to use this tool versus alternatives. It implies strategic use through 'C-suite expertise' but provides no exclusion criteria or context for when not to use 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