win_loss_decoder
Analyse Win/Loss deals — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Gapup Hub — Win/Loss 32 deals Q1 2026 · Win rate 41% → 68% potentiel · Playbook 8 actions CRO. Inputs are validated server-side — send the documented case fields.
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/win-loss-decoder.md
What win_loss_decoder does on Mcp Knowledge
AI agents call win_loss_decoder 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 |
|---|---|---|---|
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 |
deals | array | Yes | |
company | object | Yes | |
product | object | Yes | |
topCompetitors | array | — | |
primaryChallenge | string | — | |
salesCycleTargetDays | number | — |
Parameters from the server's own tool schema.
Why win_loss_decoder is rated Low
The tool performs analysis of deal data and returns a structured report/deliverable. It appears to read and process deal information to generate insights and recommendations (a playbook), with no indication of writing to external systems, executing code, or destructive/financial operations.
From the tool's definition Analyse Win/Loss deals — Returns a structured, audited deliverable. Win rate 41% → 68% potentiel · Playbook 8 actions CRO.
Risk signalsHigh parameter count (20 properties)
Attacks that exploit this kind of access
The rule that runs win_loss_decoder 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 win_loss_decoder, this is the rule to start with:
win_loss_decoder 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 win_loss_decoder call is checked against it from then on.
Questions about win_loss_decoder
Analyse Win/Loss deals — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Gapup Hub — Win/Loss 32 deals Q1 2026 · Win rate 41% → 68% potentiel · Playbook 8 actions CRO. 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.
win_loss_decoder accepts 7 parameters: async, deals, company, product, topCompetitors, primaryChallenge, salesCycleTargetDays. Required: deals, company, product. 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 win_loss_decoder: 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.
win_loss_decoder 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 win_loss_decoder 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 win_loss_decoder. 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.
win_loss_decoder 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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