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mcp-knowledge

by io.github.getgapup

capacity_planning

Read-only

Planification capacitaire — Gapup agent-payable C-suite expertise (CHRO). Returns a structured, audited deliverable. Reference case: Gapup Hub — 22→48 FTE en 12m · ARR €480k→€1.7M · Plan d'embauches par département. 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
benchmarksNo
financialsYes
constraintsNo
currentTeamYes
hiringBudgetEurNo

Schema Changelog

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

  1. Changed11 schema fields changed
    • addedInput schema / properties / benchmarks / properties
      Added value: +{
+  "arrPerEmployee": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "targetArrPerEmployee": {
+    "minimum": 0,
+    "type": "number"
+  }
+}
    • addedInput schema / properties / company / properties
      Added value: +{
+  "name": {
+    "maxLength": 100,
+    "type": "string"
+  },
+  "sector": {
+    "maxLength": 100,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / company / required
      Added value: +[
+  "name",
+  "sector"
+]
    • addedInput schema / properties / constraints / items
      Added value: +{
+  "maxLength": 200,
+  "type": "string"
+}
    • addedInput schema / properties / constraints / maxItems
      Added value: +5
    • addedInput schema / properties / currentTeam / properties
      Added value: +{
+  "departments": {
+    "items": {
+      "properties": {
+        "annualCostPerHeadEur": {
+          "minimum": 0,
+          "type": "number"
+        },
+        "currentHeadcount": {
+          "minimum": 0,
+          "type": "integer"
+        },
+        "name": {
+          "maxLength": 100,
+          "type": "string"
+        },
+        "openPositions": {
+          "minimum": 0,
+          "type": "integer"
+        },
+        "productivityMetric": {
+          "maxLength": 100,
+          "type": "string"
+        }
+      },
+      "required": [
+        "name",
+        "currentHeadcount"
+      ],
+      "type": "object"
+    },
+    "maxItems": 10,
+    "minItems": 1,
+    "type": "array"
+  },
+  "totalHeadcount": {
+    "minimum": 0,
+    "type": "integer"
+  }
+}
    • addedInput schema / properties / currentTeam / required
      Added value: +[
+  "totalHeadcount",
+  "departments"
+]
    • addedInput schema / properties / financials / properties
      Added value: +{
+  "currentArrEur": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "currentBurnEur": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "targetArrEur": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "timeframeMonths": {
+    "maximum": 36,
+    "minimum": 6,
+    "type": "integer"
+  }
+}
    • addedInput schema / properties / financials / required
      Added value: +[
+  "currentArrEur",
+  "targetArrEur",
+  "timeframeMonths"
+]
    • addedInput schema / properties / hiringBudgetEur / minimum
      Added value: +0
    • addedInput schema / required
      Added value: +[
+  "company",
+  "financials",
+  "currentTeam"
+]
  2. First observed

TDQS

B3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description adds moderate value by noting inputs are validated and output is a deliverable. However, it does not elaborate on validation behavior or response structure.

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 brief (3 sentences) and includes a reference case. It is front-loaded with the tool's purpose. However, it could be more structured with explicit sections.

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 insufficient. It does not explain return values, async behavior, or how to interpret the deliverable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 14%, and the description fails to explain any parameters beyond vague references to 'case fields'. The async parameter is not mentioned. The description does not compensate for the low schema coverage.

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

Purpose5/5

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

The description clearly states it performs capacity planning for HR/CHRO, returning a structured audited deliverable. The reference case provides concrete context. Among many sibling tools, this stands out as a specific planning tool.

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 explicit guidance on when to use or alternatives. The description only mentions inputs are validated server-side, but fails to instruct the agent on when to invoke this tool versus other planning tools.

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