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mcp-knowledge

by io.github.getgapup

anti_demissions_hr

Read-only

Bouclier anti-démissions — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: Buffer Inc — détection des at-risk parmi 80 FTEs (Q1 2026). 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.
focusNo
companyYes
signalsYes
employeesYes

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: +{
+  "fte": {
+    "minimum": 1,
+    "type": "integer"
+  },
+  "fundingStage": {
+    "type": "string"
+  },
+  "location": {
+    "type": "string"
+  },
+  "name": {
+    "maxLength": 120,
+    "minLength": 2,
+    "type": "string"
+  },
+  "sector": {
+    "maxLength": 120,
+    "minLength": 2,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / company / required
      Added value: +[
+  "name",
+  "sector",
+  "fte"
+]
    • addedInput schema / properties / employees / items
      Added value: +{
+  "properties": {
+    "compensationPercentile": {
+      "maximum": 100,
+      "minimum": 0,
+      "type": "number"
+    },
+    "id": {
+      "type": "string"
+    },
+    "lastEngagementScore": {
+      "maximum": 100,
+      "minimum": 0,
+      "type": "number"
+    },
+    "lastPerfRating": {
+      "type": "string"
+    },
+    "level": {
+      "enum": [
+        "IC",
+        "manager",
+        "director",
+        "VP",
+        "C-level"
+      ],
+      "type": "string"
+    },
+    "recentSignals": {
+      "items": {
+        "type": "string"
+      },
+      "maxItems": 10,
+      "type": "array"
+    },
+    "role": {
+      "type": "string"
+    },
+    "team": {
+      "type": "string"
+    },
+    "tenure": {
+      "minimum": 0,
+      "type": "integer"
+    }
+  },
+  "required": [
+    "id",
+    "role",
+    "level",
+    "tenure",
+    "team"
+  ],
+  "type": "object"
+}
    • addedInput schema / properties / employees / maxItems
      Added value: +200
    • addedInput schema / properties / employees / minItems
      Added value: +1
    • addedInput schema / properties / focus / maxLength
      Added value: +400
    • addedInput schema / properties / signals / properties
      Added value: +{
+  "eNPS": {
+    "maximum": 100,
+    "minimum": -100,
+    "type": "number"
+  },
+  "engagementScore": {
+    "maximum": 100,
+    "minimum": 0,
+    "type": "number"
+  },
+  "externalNoise": {
+    "items": {
+      "type": "string"
+    },
+    "maxItems": 10,
+    "type": "array"
+  },
+  "recentLayoffs": {
+    "type": "boolean"
+  },
+  "recentLeadershipChange": {
+    "type": "boolean"
+  }
+}
    • addedInput schema / required
      Added value: +[
+  "company",
+  "signals",
+  "employees"
+]
  2. First observed

TDQS

C2.8/5.0
Behavior3/5

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

The description mentions server-side input validation and a reference case, adding slight behavioral context. Annotations already provide readOnlyHint=true, so the description's added value is limited and does not contradict annotations.

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 concise but uses jargon ('Gapup agent-payable C-suite expertise (COO)') that may confuse the agent. It is front-loaded but could be more straightforward.

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 complexity of nested parameters and lack of an output schema, the description is incomplete. It does not explain what constitutes an 'at-risk' signal or describe the deliverable's format, leaving the agent underinformed.

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 low (20%), and the description does not elaborate on any parameters beyond saying 'send the documented case fields.' This fails to compensate for the schema's lack of clarity.

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 states it returns a structured, audited deliverable for detecting at-risk employees, as shown by the Buffer Inc reference. While the purpose is clear, it does not distinguish itself from sibling tools like 'churn_defender' or 'talent_poaching_risk'.

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?

There is no guidance on when to use this tool versus alternatives. No explicit when-to-use or when-not-to-use conditions are provided, leaving the agent without decision criteria.

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