Save a model to a git project with version control
AI agents use project_save to create or update resources in Engineering MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Engineering MCP Server environment.
This tool persists data (engineering diagrams) to a git repository, which is a reversible write operation. While it commits to version control (creating a new revision), the action is not destructive—previous versions remain accessible and the operation can be undone via git revert or reset.
From the tool's definition Tool name 'project_save' and description 'Save a model to a git project with version control' indicate creation/modification of project files in a version-controlled repository.
Documented attack patterns abuse exactly the kind of access project_save gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Engineering MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for project_save:
{
"version": "1",
"default": "deny",
"tools": {
"project_save": {
"limits": [
{
"counter": "project_save_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}project_save stays usable, but capped — an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
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Save a model to a git project with version control. It is categorised as a Write tool in the Engineering MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Engineering MCP Server MCP server in PolicyLayer and add a rule for project_save: 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 Engineering MCP Server. Nothing to install.
project_save is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the project_save 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_save. 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_save is provided by the Engineering MCP Server MCP server (puran-water/dexpi-sfiles-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Engineering MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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72 Engineering MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.