qa_pre_flight
Préparation Q&A investisseurs — Gapup agent-payable C-suite expertise (FUNDRAISING). Returns a structured, audited deliverable. Reference case: Agicap Série C €70M — 30 Q&A stratégiques · 8 questions pièges · Plan de préparation 21 jours. Inputs are validated server-side — send the documented cas...
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/qa-pre-flight.md
What qa_pre_flight does on Mcp Knowledge
AI agents call qa_pre_flight 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 |
round | object | Yes | |
company | object | Yes | |
founderContext | object | Yes |
Parameters from the server's own tool schema.
Why qa_pre_flight is rated Low
This tool generates investor Q&A preparation materials and structured deliverables based on input data. It is an analysis/content generation tool that reads inputs and returns structured output — no money movement, no data deletion, no code execution. The fundraising context is advisory/analytical, not transactional. Severity is medium because misuse could produce misleading investor-facing materials.
From the tool's definition Préparation Q&A investisseurs — Returns a structured, audited deliverable. Reference case: Agicap Série C €70M — 30 Q&A stratégiques · 8 questions pièges · Plan de préparation 21 jours.
Risk signalsHigh parameter count (14 properties)
Attacks that exploit this kind of access
The rule that runs qa_pre_flight 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 qa_pre_flight, this is the rule to start with:
qa_pre_flight 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 qa_pre_flight call is checked against it from then on.
Questions about qa_pre_flight
Préparation Q&A investisseurs — Gapup agent-payable C-suite expertise (FUNDRAISING). Returns a structured, audited deliverable. Reference case: Agicap Série C €70M — 30 Q&A stratégiques · 8 questions pièges · Plan de préparation 21 jours. 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.
qa_pre_flight accepts 4 parameters: async, round, company, founderContext. Required: round, company, founderContext. 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 qa_pre_flight: 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.
qa_pre_flight 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 qa_pre_flight 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 qa_pre_flight. 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.
qa_pre_flight 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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