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mcp-knowledge

by io.github.getgapup

qbr_auto

Read-only

QBR automatique CSM — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Gapup Hub × Alan — QBR Q1 2026 · Health score 82/100 · Upsell €18k détecté · Renewal low risk. Inputs are validated server-side — send the documented case fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
winsYes
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.
periodYes
companyYes
metricsYes
customerYes
challengesYes
nextQuarterGoalsYes

Schema Changelog

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

  1. Changed17 schema fields changed
    • addedInput schema / properties / challenges / items
      Added value: +{
+  "maxLength": 300,
+  "minLength": 5,
+  "type": "string"
+}
    • addedInput schema / properties / challenges / maxItems
      Added value: +4
    • addedInput schema / properties / challenges / minItems
      Added value: +0
    • addedInput schema / properties / company / properties
      Added value: +{
+  "name": {
+    "maxLength": 200,
+    "minLength": 2,
+    "type": "string"
+  },
+  "sector": {
+    "maxLength": 100,
+    "minLength": 2,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / company / required
      Added value: +[
+  "name",
+  "sector"
+]
    • addedInput schema / properties / customer / properties
      Added value: +{
+  "contractStartDate": {
+    "type": "string"
+  },
+  "contractValueEur": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "name": {
+    "maxLength": 200,
+    "minLength": 2,
+    "type": "string"
+  },
+  "sector": {
+    "maxLength": 100,
+    "minLength": 2,
+    "type": "string"
+  },
+  "tier": {
+    "enum": [
+      "strategic",
+      "enterprise",
+      "mid-market",
+      "smb"
+    ],
+    "type": "string"
+  }
+}
    • addedInput schema / properties / customer / required
      Added value: +[
+  "name",
+  "sector",
+  "tier",
+  "contractValueEur",
+  "contractStartDate"
+]
    • addedInput schema / properties / metrics / properties
      Added value: +{
+  "featureAdoptionPct": {
+    "maximum": 100,
+    "minimum": 0,
+    "type": "number"
+  },
+  "npsScore": {
+    "maximum": 10,
+    "minimum": 0,
+    "type": "number"
+  },
+  "renewalDateDays": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "supportTickets": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "usageRate": {
+    "maximum": 100,
+    "minimum": 0,
+    "type": "number"
+  }
+}
    • addedInput schema / properties / nextQuarterGoals / items
      Added value: +{
+  "maxLength": 300,
+  "minLength": 5,
+  "type": "string"
+}
    • addedInput schema / properties / nextQuarterGoals / maxItems
      Added value: +5
    • addedInput schema / properties / nextQuarterGoals / minItems
      Added value: +1
    • addedInput schema / properties / period / properties
      Added value: +{
+  "quarter": {
+    "enum": [
+      "Q1",
+      "Q2",
+      "Q3",
+      "Q4"
+    ],
+    "type": "string"
+  },
+  "year": {
+    "maximum": 2030,
+    "minimum": 2020,
+    "type": "number"
+  }
+}
    • addedInput schema / properties / period / required
      Added value: +[
+  "quarter",
+  "year"
+]
    • addedInput schema / properties / wins / items
      Added value: +{
+  "maxLength": 300,
+  "minLength": 5,
+  "type": "string"
+}
    • addedInput schema / properties / wins / maxItems
      Added value: +6
    • addedInput schema / properties / wins / minItems
      Added value: +1
    • addedInput schema / required
      Added value: +[
+  "company",
+  "customer",
+  "period",
+  "metrics",
+  "wins",
+  "challenges",
+  "nextQuarterGoals"
+]
  2. First observed

TDQS

C2.6/5.0
Behavior2/5

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

Annotations already provide readOnlyHint=true (indicating no state modification) and openWorldHint=true (output may extend schema). The description adds that it 'returns a structured, audited deliverable' and that inputs are validated server-side. However, it doesn't disclose response time, job completion behavior for async, or any side effects. With annotations already covering the core behavioral profile, the marginal value is low.

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 short (three sentences) and front-loaded with the core purpose. However, the reference case example ('Gapup Hub × Alan — QBR Q1 2026 ...') adds length without aiding tool selection or invocation. Could be trimmed to remove the example or replace it with more general guidance.

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?

The tool has complex input with nested objects (period, company, customer, metrics) and no output schema. The description does not explain the return structure, data format, or how the deliverable is organized. The example hints at fields (health score, upsell, renewal risk) but not the full output. For a tool of this complexity, the description is insufficient.

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 13% (only 'async' parameter has a description). The tool description does not explain the meaning, format, or relationships among the 8 parameters. Saying 'send the documented case fields' is too vague to guide parameter construction. The description fails to compensate for the low schema coverage.

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 automates QBR (Quarterly Business Review) for CSM and returns a structured deliverable. The reference case adds concrete context. 'QBR automatique CSM' clearly identifies the tool's purpose, and it is distinguishable from siblings like 'enps_auto' or 'knowledge_base_auto'. However, the phrase 'Gapup agent-payable C-suite expertise (CRO)' is jargon that may confuse some agents.

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 guidance on when to use this tool versus alternatives. The description merely says 'send the documented case fields' without specifying contexts, prerequisites, or scenarios where other tools might be more appropriate. Given the large sibling set, this omission significantly reduces usefulness.

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