project_tree
AI agents call project_tree to retrieve information from Pypi:pypreset without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Given the name 'project_tree' and the server's purpose of managing Python project presets, this tool most likely queries and returns a hierarchical view of a project's directory structure without modifying data. No evidence suggests code execution, data mutation, or destructive operations.
From the tool's definition Tool name 'project_tree' strongly suggests it retrieves and displays a filesystem tree structure of a project.
Attacks that exploit this kind of access
project_tree. It is categorised as a Read tool in the Pypi:pypreset MCP Server, which means it retrieves data without modifying state.
Register the Pypi:pypreset MCP server in PolicyLayer and add a rule for project_tree: 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 Pypi:pypreset. Nothing to install.
project_tree 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 project_tree 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 project_tree. 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.
project_tree is provided by the Pypi:pypreset MCP server (KaiErikNiermann/pypreset). 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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