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.
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.
Attacks that exploit this kind of access
debugpy_context. It is categorised as a Read tool in the Debugpy MCP Server, which means it retrieves data without modifying state.
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.
debugpy_context is a Read tool with low risk. Read-only tools are generally safe to allow by default.
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.
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.
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.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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