AI agents use update_component to create or update resources in PyPSA MCP — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your PyPSA MCP environment.
The tool name 'update_component' clearly indicates a reversible modification operation on energy system model components. This is characteristic of Write operations. While the empty description reduces confidence slightly, context from sibling tools (add_generator, add_line, add_load, add_component) all performing model construction/modification, and the presence of 'delete_model' as a separate destructive…
From the tool's definition Tool name 'update_component' indicates modification of existing data. Sibling tools include 'add_component' (Write), 'delete_model' (Destructive), and 'add_generator', 'add_line', 'add_load' (all Write), suggesting this tool modifies energy system model…
Documented attack patterns abuse exactly the kind of access update_component gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and PyPSA MCP, and nothing reaches the server without passing your rules. This is the rule we recommend for update_component:
{
"version": "1",
"default": "deny",
"tools": {
"update_component": {
"limits": [
{
"counter": "update_component_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}update_component 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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update_component. It is categorised as a Write tool in the PyPSA MCP MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the PyPSA MCP server in PolicyLayer and add a rule for update_component: 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 PyPSA MCP. Nothing to install.
update_component 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 update_component 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 update_component. 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.
update_component is provided by the PyPSA MCP server (open-energy-transition/pypsa-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 22 PyPSA MCP tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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22 PyPSA MCP tools catalogued and risk-classified — across an index of 42,500+ MCP servers.