start_research · Python MCP Server Template MCP

WHAT IT DOES

What start_research does on Python MCP Server Template

AI agents invoke start_research to trigger actions in Python MCP Server Template. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.

WHY IT NEEDS A POLICY

Why start_research needs a policy

This tool triggers execution of a research workflow with user-supplied parameters. While the blast radius is constrained (research tasks typically don't delete data or move money), an agent could initiate expensive/resource-intensive research operations or trigger automation with side effects. Classified as Execute rather than Write because it runs/triggers a task rather than merely storing data.

From the tool's definition Tool name 'start_research' combined with description 'Start a research task with specified parameters' indicates triggering an external operation (research task initiation).

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

start_research Rate-limited

start_research stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "start_research": {
      "limits": [
        {
          "counter": "start_research_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL →

Instant setup, no code required.

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

FAQ

Questions about start_research

What does the start_research tool do? +

Start a research task with specified parameters. It is categorised as a Execute tool in the Python MCP Server Template MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on start_research? +

Register the Python MCP Server Template MCP server in PolicyLayer and add a rule for start_research: 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 Python MCP Server Template. Nothing to install.

What risk level is start_research? +

start_research is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit start_research? +

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

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

start_research is provided by the Python MCP Server Template MCP server (raido-star/ridiculous). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.