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Glama

mcp-knowledge

by io.github.getgapup

market_sizing

Read-only

Dimensionnement marché TAM/SAM/SOM — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Gapup Hub — TAM/SAM/SOM IA décisionnelle C-suite Europe · TAM €48Md · SOM €280M Year-3. 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.
targetYes
horizonNo
productYes
approachNo
competitorCompsNo

Schema Changelog

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

  1. Changed12 schema fields changed
    • addedInput schema / properties / approach / items
      Added value: +{
+  "enum": [
+    "top-down",
+    "bottom-up",
+    "value-theory",
+    "hybrid"
+  ],
+  "type": "string"
+}
    • addedInput schema / properties / approach / maxItems
      Added value: +3
    • addedInput schema / properties / approach / minItems
      Added value: +1
    • addedInput schema / properties / competitorComps / items
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / competitorComps / maxItems
      Added value: +5
    • addedInput schema / properties / competitorComps / minItems
      Added value: +0
    • addedInput schema / properties / horizon / enum
      Added value: +[
+  "current",
+  "3-year",
+  "5-year"
+]
    • addedInput schema / properties / product / properties
      Added value: +{
+  "category": {
+    "maxLength": 120,
+    "minLength": 2,
+    "type": "string"
+  },
+  "name": {
+    "maxLength": 200,
+    "minLength": 2,
+    "type": "string"
+  },
+  "valueProposition": {
+    "maxLength": 400,
+    "minLength": 10,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / product / required
      Added value: +[
+  "name",
+  "category",
+  "valueProposition"
+]
    • addedInput schema / properties / target / properties
      Added value: +{
+  "customerType": {
+    "enum": [
+      "b2b",
+      "b2c",
+      "b2b2c",
+      "marketplace"
+    ],
+    "type": "string"
+  },
+  "geography": {
+    "maxLength": 100,
+    "minLength": 2,
+    "type": "string"
+  },
+  "segments": {
+    "items": {
+      "type": "string"
+    },
+    "maxItems": 5,
+    "minItems": 1,
+    "type": "array"
+  }
+}
    • addedInput schema / properties / target / required
      Added value: +[
+  "geography",
+  "segments",
+  "customerType"
+]
    • addedInput schema / required
      Added value: +[
+  "product",
+  "target"
+]
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint. The description adds value by noting inputs are validated server-side and the deliverable is audited. No contradiction with annotations; adds behavioral context beyond safety.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences plus a reference case; no fluff. Front-loaded with core purpose and key attributes. Efficiently communicates what the tool does.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (6 params, nested objects, no output schema), the description provides a clear deliverable type and a concrete example. It omits output format details but is sufficient for an agent to understand scope and invoke correctly.

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

Parameters3/5

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

Schema coverage is low (17%), and the description does not detail each parameter but provides a reference case with example values (e.g., TAM €48Md). This helps infer usage but leaves many parameters (like target, product, approach) unspecified beyond schema.

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?

Clearly states it performs market sizing (TAM/SAM/SOM) and returns a structured, audited deliverable. The verb 'Dimensionnement' and resource 'marché' are specific, and the reference case adds concrete context. It distinguishes from siblings by focusing on a specific CMO-oriented market sizing.

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?

Implies usage by stating inputs are validated server-side and to send documented case fields, but provides no explicit guidance on when to use this tool vs. alternatives among the many siblings. No when-not 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