Skip to main content
Glama

mcp-knowledge

by io.github.getgapup

customer_voice_synth

Read-only

Synthèse voix client — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Alan (assurance santé) — 3 personas · Top 5 douleurs · Repositionnement messagerie recommandé. 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
dataSourcesYes
targetSegmentsYes
repositioningFocusNo

Schema Changelog

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

  1. Changed11 schema fields changed
    • addedInput schema / properties / company / properties
      Added value: +{
+  "name": {
+    "maxLength": 200,
+    "minLength": 2,
+    "type": "string"
+  },
+  "product": {
+    "maxLength": 100,
+    "minLength": 2,
+    "type": "string"
+  },
+  "sector": {
+    "maxLength": 100,
+    "minLength": 2,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / company / required
      Added value: +[
+  "name",
+  "sector",
+  "product"
+]
    • addedInput schema / properties / dataSources / items
      Added value: +{
+  "properties": {
+    "description": {
+      "maxLength": 200,
+      "type": "string"
+    },
+    "type": {
+      "enum": [
+        "reviews",
+        "support-tickets",
+        "nps-verbatims",
+        "user-interviews",
+        "social",
+        "churn-surveys"
+      ],
+      "type": "string"
+    },
+    "volume": {
+      "minimum": 0,
+      "type": "number"
+    }
+  },
+  "required": [
+    "type",
+    "volume"
+  ],
+  "type": "object"
+}
    • addedInput schema / properties / dataSources / maxItems
      Added value: +6
    • addedInput schema / properties / dataSources / minItems
      Added value: +1
    • addedInput schema / properties / repositioningFocus / maxLength
      Added value: +300
    • addedInput schema / properties / repositioningFocus / minLength
      Added value: +10
    • addedInput schema / properties / targetSegments / items
      Added value: +{
+  "maxLength": 80,
+  "type": "string"
+}
    • addedInput schema / properties / targetSegments / maxItems
      Added value: +5
    • addedInput schema / properties / targetSegments / minItems
      Added value: +1
    • addedInput schema / required
      Added value: +[
+  "company",
+  "dataSources",
+  "targetSegments"
+]
  2. First observed

TDQS

C2.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds that inputs are validated server-side and returns a structured deliverable, which is consistent and adds minor 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.

Conciseness3/5

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

The description is relatively short but mixes languages (French/English) and includes a specific reference case. It could be more structured and focused.

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?

For a tool with 5 parameters, nested objects, and no output schema, the description is incomplete. It does not clarify output format or the role of all parameters like 'repositioningFocus'.

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 coverage is 20%, but the description does not explain parameters beyond listing required fields. The reference case (Alan) gives an implicit example but does not add explicit meaning to the schema.

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 the tool produces a 'synthèse voix client' (customer voice synthesis) and returns a structured, audited deliverable for C-suite/CMO. The purpose is clear but does not distinguish from sibling tools like brand_equity_voice_share_calculator.

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 this tool versus alternatives. The target audience (CMO) is mentioned, but no exclusions or prerequisites are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All mcp-knowledge toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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