save_plays
Plans de sauvetage clients — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Kyriba — Plan sauvetage 30j · ARR €11.988 · Champion parti · Script 6 actions · 3 concessions. Inputs are validated server-side — send the documented case fields.
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/save-plays.md
What save_plays does on Mcp Knowledge
AI agents use save_plays to create or update resources in Mcp Knowledge, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Mcp Knowledge environment.
| 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 |
account | object | Yes | |
company | object | Yes | |
product | object | Yes |
Parameters from the server's own tool schema.
Why save_plays is rated Medium
This tool creates and structures customer retention/rescue plans with documented deliverables and action scripts. The reference case shows it produces audited outputs that influence business decisions (champion selection, concession terms, 30-day timelines). This is a Write operation because it generates and modifies reversible business artifacts (plans, scripts, case documentation).
From the tool's definition 'Plans de sauvetage clients' (customer rescue plans); 'Returns a structured, audited deliverable'; 'Script 6 actions'; 'send the documented case fields' — indicates creation and modification of structured business plans and action scripts tied to customer…
Risk signalsHigh parameter count (13 properties)
Attacks that exploit this kind of access
The rule that runs save_plays 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 save_plays, this is the rule to start with:
save_plays stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. 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 save_plays call is checked against it from then on.
Questions about save_plays
Plans de sauvetage clients — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Kyriba — Plan sauvetage 30j · ARR €11.988 · Champion parti · Script 6 actions · 3 concessions. Inputs are validated server-side — send the documented case fields. It is categorised as a Write tool in the Mcp Knowledge MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
save_plays accepts 4 parameters: async, account, company, product. Required: account, 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 save_plays: 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.
save_plays is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the save_plays 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 save_plays. 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.
save_plays 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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