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mcp-knowledge

by io.github.getgapup

deal_coach

Read-only

Coach de deal MEDDIC — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Datadog Enterprise deal Société Générale €1.2M ARR — coaching MEDDIC + escalation plays + 14 next actions. 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.
focusNo
knownContextYes
buyingCommitteeYes

Schema Changelog

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

  1. Changed9 schema fields changed
    • addedInput schema / properties / buyingCommittee / items
      Added value: +{
+  "properties": {
+    "name": {
+      "type": "string"
+    },
+    "role": {
+      "enum": [
+        "champion",
+        "economic-buyer",
+        "user",
+        "influencer",
+        "blocker"
+      ],
+      "type": "string"
+    },
+    "title": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "name",
+    "title",
+    "role"
+  ],
+  "type": "object"
+}
    • addedInput schema / properties / buyingCommittee / maxItems
      Added value: +8
    • addedInput schema / properties / buyingCommittee / minItems
      Added value: +2
    • addedInput schema / properties / deal / properties
      Added value: +{
+  "accountName": {
+    "type": "string"
+  },
+  "amountEur": {
+    "type": "number"
+  },
+  "daysInStage": {
+    "type": "integer"
+  },
+  "expectedCloseDate": {
+    "type": "string"
+  },
+  "ownerRep": {
+    "type": "string"
+  },
+  "stage": {
+    "type": "string"
+  }
+}
    • addedInput schema / properties / deal / required
      Added value: +[
+  "accountName",
+  "amountEur",
+  "stage",
+  "daysInStage",
+  "expectedCloseDate",
+  "ownerRep"
+]
    • addedInput schema / properties / focus
      Added value: +{
+  "maxLength": 400,
+  "type": "string"
+}
    • addedInput schema / properties / knownContext / maxLength
      Added value: +800
    • addedInput schema / properties / knownContext / minLength
      Added value: +20
    • addedInput schema / required
      Added value: +[
+  "deal",
+  "buyingCommittee",
+  "knownContext"
+]
  2. First observed

TDQS

C2.8/5.0
Behavior3/5

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

Annotations declare readOnlyHint=true and openWorldHint=true, which are consistent with an analysis tool. The description adds that inputs are validated server-side but does not discuss any side effects or async behavior for the async parameter, which is documented in the schema, not the description.

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 but contains a mix of languages and a reference case that may not be universally understood. It is not overly verbose, but the structure could be improved for clarity.

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 (5 parameters, nested objects, no output schema), the description is insufficient. It does not explain the return value format or how the tool fits with sibling tools like meddic_scoring. The agent would lack critical information to use the tool effectively.

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 20% (only the async parameter has a description). The description vaguely references 'documented case fields' but does not explain the purpose or constraints of the deal, buyingCommittee, knownContext, or focus parameters. This leaves the agent without sufficient context for correct parameter usage.

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 coaches deals using MEDDIC methodology and returns a structured deliverable. It mentions specific techniques like escalation plays and next actions, making the purpose reasonably clear. However, the exact output format is vague.

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?

There is no guidance on when to use this tool versus alternatives like meddic_scoring or deal_structurer. The description implies it's for enterprise deals but lacks explicit when-to-use or when-not-to-use guidance.

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