competitor_recommendations
Recommandations concurrentielles — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Answers: Given my competitors, what strategic actions should I take and in what order? · What should my 7/30/90/180-day competitive response plan look like? Reference case: N...
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/competitor-recommendations.md
What competitor_recommendations does on Mcp Knowledge
AI agents call competitor_recommendations 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 |
focus | string | — | |
competitors | array | Yes | |
selfCompany | object | Yes |
Parameters from the server's own tool schema.
Why competitor_recommendations is rated Low
This tool returns strategic recommendations and analysis based on competitor inputs. It queries/analyzes data and returns structured advisory content (CMO-level expertise). No data is written, deleted, or executed — it is a read/analysis operation.
From the tool's definition Returns a structured, audited deliverable... what strategic actions should I take... competitive response plan
Risk signalsAccepts URL/endpoint input (selfCompany.url)
Attacks that exploit this kind of access
The rule that runs competitor_recommendations 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 competitor_recommendations, this is the rule to start with:
competitor_recommendations 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 competitor_recommendations call is checked against it from then on.
Questions about competitor_recommendations
Recommandations concurrentielles — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Answers: Given my competitors, what strategic actions should I take and in what order? · What should my 7/30/90/180-day competitive response plan look like? Reference case: Notion — actions face à ClickUp, Asana, Coda. 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.
competitor_recommendations accepts 4 parameters: async, focus, competitors, selfCompany. Required: competitors, selfCompany. 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 competitor_recommendations: 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.
competitor_recommendations 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 competitor_recommendations 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 competitor_recommendations. 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.
competitor_recommendations 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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