config_reset
Reset configuration to defaults Use when native settings.json edits are wrong because the values need to be read by the Ruflo runtime (daemon, MCP server, neural router) — those load via the config_* path, not by re-reading settings.json. For .gitignore / .editorconfig style files, native Edit is...
This record as markdown: /tools/io-github-ruvnet-claude-flow/config-reset.md
What config_reset does on Claude Flow
AI agents call config_reset to permanently remove resources in Claude Flow, typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
Why config_reset is rated Critical
Resetting configuration to defaults destroys the current configuration irreversibly unless separately backed up. The affected components (daemon, MCP server, neural router) indicate a high blast radius — misconfiguration or unintended reset could disrupt enterprise orchestration services. This maps to Destructive as the current config is overwritten and cannot be recovered from this action alone.
From the tool's definition 'Reset configuration to defaults' — this overwrites current configuration state with defaults, which is irreversible without a prior backup. The description notes it affects runtime components (daemon, MCP server, neural router), amplifying blast radius.
Attacks that exploit this kind of access
The rule that runs config_reset safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Claude Flow, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For config_reset, this is the rule to start with:
config_reset is removed from the agent's tool list entirely, so the agent never calls it. The rest of the server keeps working.
The button opens the PolicyLayer dashboard: create your workspace, connect Claude Flow, apply this rule, and every config_reset call is checked against it from then on.
Questions about config_reset
Reset configuration to defaults Use when native settings.json edits are wrong because the values need to be read by the Ruflo runtime (daemon, MCP server, neural router) — those load via the config_* path, not by re-reading settings.json. For .gitignore / .editorconfig style files, native Edit is fine. It is categorised as a Destructive tool in the Claude Flow MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Claude Flow MCP server in PolicyLayer and add a rule for config_reset: 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 Claude Flow. Nothing to install.
config_reset is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the config_reset 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 config_reset. 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.
config_reset is provided by the Claude Flow MCP server (claude-flow). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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