Medium Risk

earnings_reviewer

Earnings Reviewer — Gapup agent-payable C-suite expertise (FUNDRAISING). Returns a structured, audited deliverable. Reference case: Salesforce Q3 FY2026 — call transcript + 10-Q + guidance → analyst note. Inputs are validated server-side — send the documented case fields.

Risk signalsHigh parameter count (12 properties)

Part of the Mcp Knowledge server.

earnings_reviewer can modify Mcp Knowledge data, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents use earnings_reviewer to create or modify resources in Mcp Knowledge. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.

Without a policy, an AI agent could call earnings_reviewer repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Mcp Knowledge.

Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "earnings_reviewer": {
      "limits": [
        {
          "counter": "earnings_reviewer_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

See the full Mcp Knowledge policy for all 271 tools.

Get this rule live on your own Mcp Knowledge server in minutes. PolicyLayer enforces it on every call, before it runs.

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These attack patterns abuse exactly the kind of access earnings_reviewer gives an agent. Each links to the full case and the policy that stops it:

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Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so earnings_reviewer only ever does what you allow.

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Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.

What does the earnings_reviewer tool do? +

Earnings Reviewer — Gapup agent-payable C-suite expertise (FUNDRAISING). Returns a structured, audited deliverable. Reference case: Salesforce Q3 FY2026 — call transcript + 10-Q + guidance → analyst note. 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.

How do I enforce a policy on earnings_reviewer? +

Register the Mcp Knowledge MCP server in PolicyLayer and add a rule for earnings_reviewer: 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.

What risk level is earnings_reviewer? +

earnings_reviewer is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit earnings_reviewer? +

Yes. Add a rate_limit block to the earnings_reviewer 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.

How do I block earnings_reviewer completely? +

Set action: deny in the PolicyLayer policy for earnings_reviewer. 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.

What MCP server provides earnings_reviewer? +

earnings_reviewer 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.

Enforce policy on every Mcp Knowledge tool call.

Deterministic rules across all 271 Mcp Knowledge tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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