delete_study

THE RISK

Critical Risk

Why delete_study needs a policy

This tool irreversibly removes data (studies and associated interview data). While the blast radius is somewhat limited to a single study rather than system-wide, the permanent loss of research data, user responses, and insights represents a high-severity destructive action. An AI agent misusing this could destroy valuable research datasets and user interview records that cannot be recovered.

From the tool's definition Tool description states 'Permanently deletes a study and all associated data' — the word 'Permanently' and 'deletes' are explicit indicators of irreversible destruction.

Documented attack patterns abuse exactly the kind of access delete_study gives an agent:

POLICY

How to control delete_study

PolicyLayer is an MCP gateway — it sits between your AI agents and Mcp Usercall, and nothing reaches the server without passing your rules. This is the rule we recommend for delete_study:

policy.json

{
  "version": "1",
  "default": "deny",
  "hide": [
    "delete_study"
  ]
}

delete_study disappears from the agent's tool list entirely, and any attempt to call it is denied. The rest of the server keeps working.

Create a free account and register Mcp Usercall — nothing to install.
Add this policy — paste it, or build it visually.
Point your MCP client (Claude, Cursor, anything) at your gateway URL.

RESTRICT THIS TOOL →

Free to start. No card required.

EXPLORE

Related tools and policies

More Mcp Usercall tools

Write create_study Creates a user interview study and returns an interview_link to share with participants. S Write update_study Updates an existing study. Use this to increase interview slots, add/update media, or modi Read get_study_results Returns analysis results. When presenting results, always quote specific participant respo Read get_study_status get_study_status

All 5 Mcp Usercall tools →

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Go deeper

Critical-risk tools in Mcp Usercall → Every critical-risk tool this server exposes, in one view.
All critical-risk MCP tools → Every critical-risk tool across the catalogue, and the policy pattern that contains them.
The MCP Attack Database → Documented attack patterns against MCP deployments — and the policies that stop them.
Blast radius → How much damage one compromised or confused agent can do — and how to bound it.
Tool call approval → Approval gates on individual tool calls: auto-allow, prompt, or deny.
Rate Limiting MCP Tool Calls: A Practical Guide →
MCP Security: Why Prompt Guardrails Aren't Enough →

FAQ

Questions about delete_study

What does the delete_study tool do? +

Permanently deletes a study and all associated data. Releases unused reserved credits. It is categorised as a Destructive tool in the Mcp Usercall MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.

How do I enforce a policy on delete_study? +

Register the Mcp Usercall MCP server in PolicyLayer and add a rule for delete_study: 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 Usercall. Nothing to install.

What risk level is delete_study? +

delete_study is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.

Can I rate-limit delete_study? +

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

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

delete_study is provided by the Mcp Usercall MCP server (junetic/usercall-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Mcp Usercall tool call.

Start from Mcp Usercall, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

CHECK YOUR STACK →