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Glama

mcp-knowledge

by io.github.getgapup

champion_mapping

Read-only

Cartographie du champion — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Spendesk × Decathlon (deal €120k/an) — Champion identifié : CFO Group · Plan 6 semaines multi-touch. Inputs are validated server-side — send the documented case fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dealYes
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.
knownContactsYes
sellerContextYes

Schema Changelog

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

  1. Changed8 schema fields changed
    • addedInput schema / properties / deal / properties
      Added value: +{
+  "companyName": {
+    "maxLength": 200,
+    "minLength": 2,
+    "type": "string"
+  },
+  "dealSizeEur": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "sector": {
+    "maxLength": 100,
+    "minLength": 2,
+    "type": "string"
+  },
+  "stage": {
+    "maxLength": 60,
+    "minLength": 2,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / deal / required
      Added value: +[
+  "companyName",
+  "sector",
+  "dealSizeEur",
+  "stage"
+]
    • addedInput schema / properties / knownContacts / items
      Added value: +{
+  "properties": {
+    "department": {
+      "maxLength": 60,
+      "minLength": 2,
+      "type": "string"
+    },
+    "engagementLevel": {
+      "enum": [
+        "none",
+        "low",
+        "medium",
+        "high"
+      ],
+      "type": "string"
+    },
+    "name": {
+      "maxLength": 100,
+      "minLength": 2,
+      "type": "string"
+    },
+    "notes": {
+      "maxLength": 300,
+      "type": "string"
+    },
+    "title": {
+      "maxLength": 100,
+      "minLength": 2,
+      "type": "string"
+    }
+  },
+  "required": [
+    "name",
+    "title",
+    "department",
+    "engagementLevel"
+  ],
+  "type": "object"
+}
    • addedInput schema / properties / knownContacts / maxItems
      Added value: +10
    • addedInput schema / properties / knownContacts / minItems
      Added value: +1
    • addedInput schema / properties / sellerContext / properties
      Added value: +{
+  "dealVelocityDays": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "mainValueProp": {
+    "maxLength": 300,
+    "minLength": 10,
+    "type": "string"
+  },
+  "ourProductName": {
+    "maxLength": 100,
+    "minLength": 2,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / sellerContext / required
      Added value: +[
+  "ourProductName",
+  "mainValueProp"
+]
    • addedInput schema / required
      Added value: +[
+  "deal",
+  "knownContacts",
+  "sellerContext"
+]
  2. First observed

TDQS

B3.4/5.0
Behavior3/5

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

The annotations (readOnlyHint: true, openWorldHint: true) already indicate no mutations and flexible inputs. The description adds 'inputs are validated server-side' and confirms a deliverable is returned, but does not detail any potential side effects, authorization requirements, or return format beyond 'structured, audited deliverable.'

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 two substantive sentences and a reference case, which is concise. However, the specific example may not be universally helpful and could be shortened. The purpose is front-loaded effectively.

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 4 parameters including complex nested objects and no output schema. The description does not clarify the return structure (e.g., champion name, plan timeline), how to handle the `async` parameter, or what constitutes a successful result. The vagueness of 'structured, audited deliverable' leaves significant gaps for the agent.

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?

With schema description coverage at 25%, the description should clarify parameter usage but only says 'send the documented case fields' and mentions validation. It does not explain the `async` parameter (which has a schema description) or the nested fields in `deal`, `knownContacts`, and `sellerContext`. The reference case is illustrative but does not map to parameters.

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?

The description clearly states the tool's purpose: mapping champions for CRO-level deals, returning a structured deliverable. It includes a specific reference case (Spendesk × Decathlon) which adds concreteness and helps distinguish from siblings.

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 usage for upmarket CRO deals with the phrase 'Gapup agent-payable C-suite expertise (CRO)', but it does not explicitly state when to use this tool versus alternative sales tools like competitive_deep_dive or deal_coach. The context of champion mapping is implicit rather than prescriptive.

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