press_influencer
Presse & influenceurs — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Agicap (levée Série C €70M) — CP + 12 contacts presse Tier-1 · plan de diffusion 14 jours. Inputs are validated server-side — send the documented case fields.
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/press-influencer.md
What press_influencer does on Mcp Knowledge
AI agents use press_influencer 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 |
budget | number | — | |
company | object | Yes | |
targetMedia | array | Yes | |
announcement | object | Yes | |
targetAudience | string | Yes |
Parameters from the server's own tool schema.
Why press_influencer is rated Medium
The tool appears to generate press releases and media contact plans (content creation/write deliverables). The reference to 'agent-payable C-suite expertise' suggests a potential financial component, but the description focuses on content output (press release + distribution plan) rather than explicitly moving money.
From the tool's definition Returns a structured, audited deliverable... CP + 12 contacts presse Tier-1 · plan de diffusion 14 jours
Risk signalsHigh parameter count (11 properties)
Attacks that exploit this kind of access
The rule that runs press_influencer 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 press_influencer, this is the rule to start with:
press_influencer 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 press_influencer call is checked against it from then on.
Questions about press_influencer
Presse & influenceurs — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Agicap (levée Série C €70M) — CP + 12 contacts presse Tier-1 · plan de diffusion 14 jours. 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.
press_influencer accepts 6 parameters: async, budget, company, targetMedia, announcement, targetAudience. Required: company, targetMedia, announcement, targetAudience. 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 press_influencer: 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.
press_influencer 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 press_influencer 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 press_influencer. 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.
press_influencer 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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