Import a model from various formats (JSON, Proteus XML, SFILES string) - Replaces dexpi_import_json, dexpi_import_proteus_xml, sfiles_from_string
AI agents use model_load 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 imports/loads data into the engineering model, creating or replacing the in-memory or persisted model state. It is a Write operation as it modifies the current working model by loading new content. It does not execute code or irreversibly destroy data (a new import could be undone by re-importing), making Write the most appropriate category.
From the tool's definition Import a model from various formats (JSON, Proteus XML, SFILES string) - Replaces dexpi_import_json, dexpi_import_proteus_xml, sfiles_from_string
Documented attack patterns abuse exactly the kind of access model_load 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 model_load:
{
"version": "1",
"default": "deny",
"tools": {
"model_load": {
"limits": [
{
"counter": "model_load_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}model_load 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.
Free to start. No card required.
Import a model from various formats (JSON, Proteus XML, SFILES string) - Replaces dexpi_import_json, dexpi_import_proteus_xml, sfiles_from_string. 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 model_load: 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.
model_load 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 model_load 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 model_load. 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.
model_load 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.
Free to start. No card required.
72 Engineering MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.