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mcp-knowledge

by io.github.getgapup

win_loss_decoder

Read-only

Analyse Win/Loss deals — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Gapup Hub — Win/Loss 32 deals Q1 2026 · Win rate 41% → 68% potentiel · Playbook 8 actions CRO. 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.
dealsYes
companyYes
productYes
topCompetitorsNo
primaryChallengeNo
salesCycleTargetDaysNo

Schema Changelog

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

  1. Changed15 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 / deals / items
      Added value: +{
+  "properties": {
+    "competitor": {
+      "maxLength": 100,
+      "type": "string"
+    },
+    "dealValueEur": {
+      "minimum": 0,
+      "type": "number"
+    },
+    "durationDays": {
+      "minimum": 1,
+      "type": "number"
+    },
+    "id": {
+      "maxLength": 50,
+      "minLength": 1,
+      "type": "string"
+    },
+    "lostReason": {
+      "maxLength": 300,
+      "type": "string"
+    },
+    "notes": {
+      "maxLength": 500,
+      "type": "string"
+    },
+    "outcome": {
+      "enum": [
+        "won",
+        "lost",
+        "ghosted",
+        "no-decision"
+      ],
+      "type": "string"
+    }
+  },
+  "required": [
+    "id",
+    "outcome",
+    "dealValueEur",
+    "durationDays"
+  ],
+  "type": "object"
+}
    • addedInput schema / properties / deals / maxItems
      Added value: +50
    • addedInput schema / properties / deals / minItems
      Added value: +3
    • addedInput schema / properties / primaryChallenge / maxLength
      Added value: +400
    • addedInput schema / properties / primaryChallenge / minLength
      Added value: +10
    • addedInput schema / properties / product / properties
      Added value: +{
+  "avgContractValueEur": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "category": {
+    "maxLength": 120,
+    "minLength": 2,
+    "type": "string"
+  },
+  "name": {
+    "maxLength": 200,
+    "minLength": 2,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / product / required
      Added value: +[
+  "name",
+  "category",
+  "avgContractValueEur"
+]
    • addedInput schema / properties / salesCycleTargetDays / maximum
      Added value: +730
    • addedInput schema / properties / salesCycleTargetDays / minimum
      Added value: +1
    • addedInput schema / properties / topCompetitors / items
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / topCompetitors / maxItems
      Added value: +5
    • addedInput schema / properties / topCompetitors / minItems
      Added value: +0
    • addedInput schema / required
      Added value: +[
+  "company",
+  "product",
+  "deals"
+]
  2. First observed

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint and openWorldHint. The description adds that inputs are validated server-side and the output is an audited deliverable, which is useful but does not disclose async behavior (despite an async parameter) or rate limits. It provides moderate additional context beyond 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 concise (4 sentences) and front-loaded with the purpose. The reference case is a nice touch but could be omitted without losing core meaning. No wasted words.

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 (7 parameters, nested objects, no output schema), the description is too brief. It does not describe the return format or expected behavior for the async parameter, leaving significant gaps for the agent to infer.

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 (14%), yet the description does not explain any parameters beyond a vague 'send the documented case fields'. It fails to compensate for the lack of schema descriptions, leaving the agent unclear on what each field means.

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 analyzes Win/Loss deals and returns a structured deliverable. However, it does not explicitly distinguish itself from sibling tools like deal_coach or competitive_deep_dive, which could also analyze deals. The reference to C-suite expertise adds specificity but not differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for win/loss analysis but provides no explicit guidance on when to use it versus alternatives, nor does it mention when not to use it. The context is clear but lacks exclusion criteria or alternative tool references.

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