deal_coach
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 fie...
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/deal-coach.md
What deal_coach does on Mcp Knowledge
AI agents call deal_coach to retrieve information from Mcp Knowledge without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
deal | object | Yes | |
async | boolean | — | If 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 ti |
focus | string | — | |
knownContext | string | Yes | |
buyingCommittee | array | Yes |
Parameters from the server's own tool schema.
Why deal_coach is rated Low
The tool appears to perform sales deal coaching and analysis using the MEDDIC framework, returning structured advisory content (coaching, escalation plays, action recommendations). It reads/analyzes deal data and produces a report. There is no indication it executes transactions, modifies data, or moves money — it provides expert advisory output.
From the tool's definition 'Coach de deal MEDDIC' — returns 'a structured, audited deliverable' and '14 next actions'; reference case describes analysis/coaching output, not execution of transactions
Risk signalsHigh parameter count (14 properties)
Attacks that exploit this kind of access
The rule that runs deal_coach safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Mcp Knowledge, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For deal_coach, this is the rule to start with:
deal_coach is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Mcp Knowledge, apply this rule, and every deal_coach call is checked against it from then on.
Questions about deal_coach
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. It is categorised as a Read tool in the Mcp Knowledge MCP Server, which means it retrieves data without modifying state.
deal_coach accepts 5 parameters: deal, async, focus, knownContext, buyingCommittee. Required: deal, knownContext, buyingCommittee. The full parameter table on this page comes from the server's own tool schema.
Register the Mcp Knowledge MCP server in PolicyLayer and add a rule for deal_coach: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Mcp Knowledge. Nothing to install.
deal_coach is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the deal_coach rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for deal_coach. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
deal_coach is provided by the Mcp Knowledge MCP server (https://mcp.gapup.io). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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