knowledge_base_auto
Base de connaissance automatique — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: Klarna — knowledge base auto · Slack+Notion+Drive · 12 articles seed + structure 8 catégories. Inputs are validated server-side — send the documented case fie...
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/knowledge-base-auto.md
What knowledge_base_auto does on Mcp Knowledge
AI agents use knowledge_base_auto 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 |
focus | string | — | |
company | object | Yes | |
sources | array | Yes | |
topPainPoints | string | Yes |
Parameters from the server's own tool schema.
Why knowledge_base_auto is rated Medium
The tool appears to generate and populate a knowledge base with structured content (articles, categories) written into connected platforms like Notion and Drive. This is a Write operation — creating new content artifacts — rather than purely reading. No evidence of code execution, deletion, or financial transactions.
From the tool's definition 'Returns a structured, audited deliverable' and '12 articles seed + structure 8 catégories' — implies creating/writing structured knowledge base content (articles, categories) into connected systems (Slack+Notion+Drive)
Risk signalsHigh parameter count (11 properties)
Attacks that exploit this kind of access
The rule that runs knowledge_base_auto 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 knowledge_base_auto, this is the rule to start with:
knowledge_base_auto 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 knowledge_base_auto call is checked against it from then on.
Questions about knowledge_base_auto
Base de connaissance automatique — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: Klarna — knowledge base auto · Slack+Notion+Drive · 12 articles seed + structure 8 catégories. 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.
knowledge_base_auto accepts 5 parameters: async, focus, company, sources, topPainPoints. Required: company, sources, topPainPoints. 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 knowledge_base_auto: 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.
knowledge_base_auto 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 knowledge_base_auto 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 knowledge_base_auto. 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.
knowledge_base_auto 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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