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mcp-knowledge

by io.github.getgapup

comp_plan_architect

Read-only

Architecture plan de commissionnement — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Gapup Hub — Comp Plan 8 rôles commerciaux · OTE €65-280k · Budget comp €2.1M · Quota coverage 3.2×. 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
targetsYes
geographyNo
salesTeamYes
currentChallengesYes
preferredStructureNo

Schema Changelog

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

  1. Changed13 schema fields changed
    • addedInput schema / properties / company / properties
      Added value: +{
+  "name": {
+    "maxLength": 200,
+    "minLength": 2,
+    "type": "string"
+  },
+  "sector": {
+    "maxLength": 100,
+    "minLength": 2,
+    "type": "string"
+  },
+  "stage": {
+    "enum": [
+      "pre-seed",
+      "seed",
+      "serie-a",
+      "serie-b",
+      "serie-c",
+      "serie-d-plus",
+      "profitable-sme",
+      "enterprise"
+    ],
+    "type": "string"
+  }
+}
    • addedInput schema / properties / company / required
      Added value: +[
+  "name",
+  "sector",
+  "stage"
+]
    • addedInput schema / properties / currentChallenges / items
      Added value: +{
+  "maxLength": 200,
+  "minLength": 5,
+  "type": "string"
+}
    • addedInput schema / properties / currentChallenges / maxItems
      Added value: +5
    • addedInput schema / properties / currentChallenges / minItems
      Added value: +1
    • addedInput schema / properties / geography / maxLength
      Added value: +100
    • addedInput schema / properties / geography / minLength
      Added value: +2
    • addedInput schema / properties / preferredStructure / enum
      Added value: +[
+  "base-plus-commission",
+  "ote-split",
+  "revenue-share",
+  "milestone-bonus",
+  "hybrid"
+]
    • addedInput schema / properties / salesTeam / properties
      Added value: +{
+  "roles": {
+    "items": {
+      "properties": {
+        "currentBaseSalary": {
+          "minimum": 0,
+          "type": "number"
+        },
+        "currentOteSalary": {
+          "minimum": 0,
+          "type": "number"
+        },
+        "headcount": {
+          "maximum": 500,
+          "minimum": 1,
+          "type": "number"
+        },
+        "role": {
+          "enum": [
+            "ae",
+            "sdr",
+            "csm",
+            "se",
+            "manager",
+            "vp"
+          ],
+          "type": "string"
+        }
+      },
+      "required": [
+        "role",
+        "headcount"
+      ],
+      "type": "object"
+    },
+    "maxItems": 8,
+    "minItems": 1,
+    "type": "array"
+  },
+  "totalSalesHeadcount": {
+    "maximum": 1000,
+    "minimum": 1,
+    "type": "number"
+  }
+}
    • addedInput schema / properties / salesTeam / required
      Added value: +[
+  "roles",
+  "totalSalesHeadcount"
+]
    • addedInput schema / properties / targets / properties
      Added value: +{
+  "annualRevenueTargetEur": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "averageContractValueEur": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "churnTargetPct": {
+    "maximum": 100,
+    "minimum": 0,
+    "type": "number"
+  },
+  "salesCycleTargetDays": {
+    "maximum": 730,
+    "minimum": 1,
+    "type": "number"
+  }
+}
    • addedInput schema / properties / targets / required
      Added value: +[
+  "annualRevenueTargetEur",
+  "averageContractValueEur",
+  "salesCycleTargetDays"
+]
    • addedInput schema / required
      Added value: +[
+  "company",
+  "salesTeam",
+  "targets",
+  "currentChallenges"
+]
  2. First observed

TDQS

C2.9/5.0
Behavior3/5

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

Annotations already indicate readOnlyHint=true (safe read) and openWorldHint=true (uses real-world data). The description adds that inputs are validated server-side and returns a 'structured, audited deliverable'. This provides some behavioral context beyond annotations, but does not disclose specifics like what gets destroyed or rate limits. No contradiction with 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 short and front-loaded with the purpose. It includes a reference case for context. However, the mix of French and English and the lack of structured formatting (e.g., bullet points) slightly reduce clarity. Overall, it is concise and each sentence adds value.

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 (7 parameters, nested objects, no output schema), the description is incomplete. It does not describe the return format or what the 'structured, audited deliverable' contains. The reference case provides some context but is not sufficient to fully understand the tool's capabilities and output. The lack of output schema exacerbates the gap.

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 very low (14%)—only the 'async' parameter has a description. The main parameters (company, salesTeam, targets, currentChallenges) lack descriptions. The description says 'send the documented case fields' but does not add meaning to individual parameters. The agent must rely on schema names, which are somewhat self-explanatory but insufficient for correct invocation.

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 the tool's purpose: 'Architecture plan de commissionnement' (compensation plan architecture) and mentions it returns a structured, audited deliverable. The reference case provides context. However, it doesn't explicitly differentiate from sibling tools like comp_benchmark_geo_delta, leaving some ambiguity. Overall, purpose is clear but not maximally specific.

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 does not provide explicit guidance on when to use this tool versus alternatives. No when-to-use, when-not-to-use, or prerequisite conditions are stated. The sibling list includes many competitive analysis tools, but no differentiation is offered.

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