champion_mapping
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.
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/champion-mapping.md
What champion_mapping does on Mcp Knowledge
AI agents call champion_mapping 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 |
knownContacts | array | Yes | |
sellerContext | object | Yes |
Parameters from the server's own tool schema.
Why champion_mapping is rated Low
The tool appears to perform analysis and return structured deliverables about champion mapping (identifying key stakeholders in sales deals). It reads/analyzes input case fields and returns an audited report. No write, execute, destructive, or financial operations are described. The financial figure (€120k/an) is a reference case example, not a financial transaction.
From the tool's definition 'Cartographie du champion' (champion mapping), 'Returns a structured, audited deliverable', reference to identifying a champion (CFO Group) and a multi-touch plan — this is an analytical/reporting tool that returns structured data
Risk signalsHigh parameter count (16 properties)
Attacks that exploit this kind of access
The rule that runs champion_mapping 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 champion_mapping, this is the rule to start with:
champion_mapping 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 champion_mapping call is checked against it from then on.
Questions about champion_mapping
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. It is categorised as a Read tool in the Mcp Knowledge MCP Server, which means it retrieves data without modifying state.
champion_mapping accepts 4 parameters: deal, async, knownContacts, sellerContext. Required: deal, knownContacts, sellerContext. 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 champion_mapping: 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.
champion_mapping 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 champion_mapping 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 champion_mapping. 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.
champion_mapping 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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