add_servers_to_tenant · Langfuse Observability MCP

WHAT IT DOES

What add_servers_to_tenant does on Langfuse Observability

AI agents use add_servers_to_tenant to create or update resources in Langfuse Observability — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Langfuse Observability environment.

WHY IT NEEDS A POLICY

Why add_servers_to_tenant needs a policy

This tool creates or modifies tenant-level configurations by adding MCP servers. While it doesn't retrieve data (Read), execute arbitrary code (Execute), delete data (Destructive), or move money (Financial), it does make persistent changes to system configuration. The ability to add servers to a tenant could expand a tenant's capabilities or expose them to new operational risks, making it a Write operation.

From the tool's definition Tool name 'add_servers_to_tenant' with description 'Add MCP servers to a tenant' indicates creation/modification of tenant configuration by associating new servers with a tenant.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

add_servers_to_tenant Rate-limited

add_servers_to_tenant 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "add_servers_to_tenant": {
      "limits": [
        {
          "counter": "add_servers_to_tenant_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

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Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Langfuse Observability, so you can run agents against it safely.

FAQ

Questions about add_servers_to_tenant

What does the add_servers_to_tenant tool do? +

Add MCP servers to a tenant. It is categorised as a Write tool in the Langfuse Observability MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on add_servers_to_tenant? +

Register the Langfuse Observability MCP server in PolicyLayer and add a rule for add_servers_to_tenant: 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 Langfuse Observability. Nothing to install.

What risk level is add_servers_to_tenant? +

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

Can I rate-limit add_servers_to_tenant? +

Yes. Add a rate_limit block to the add_servers_to_tenant 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 add_servers_to_tenant completely? +

Set action: deny in the PolicyLayer policy for add_servers_to_tenant. 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 add_servers_to_tenant? +

add_servers_to_tenant is provided by the Langfuse Observability MCP server (langfuse-observability-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.