debugpy_context

debugpy_context

Server Debugpy will-garrett/debugpy-mcp
Category Read
Risk class Low
Parameters 00 required

What debugpy_context does on Debugpy

AI agents call debugpy_context to retrieve information from Debugpy without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

Why debugpy_context needs a policy

The tool name suggests it retrieves context information from a debugpy debugging session, which would be a Read operation. However, given the empty description and the nature of the sibling tools (which include execute-like capabilities such as evaluate and inject), there is uncertainty. Classified as Read with low confidence due to the uninformative description.

From the tool's definition Tool description is empty; name 'debugpy_context' suggests retrieving debug context (stack frames, variables, current state) from a debugging session.

Questions about debugpy_context

What does the debugpy_context tool do? +

debugpy_context. It is categorised as a Read tool in the Debugpy MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on debugpy_context? +

Register the Debugpy MCP server in PolicyLayer and add a rule for debugpy_context: 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 Debugpy. Nothing to install.

What risk level is debugpy_context? +

debugpy_context is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit debugpy_context? +

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

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

debugpy_context is provided by the Debugpy MCP server (will-garrett/debugpy-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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