fork_agent_character
Fork a public agent character to your library by hash. Voice config + persona are copied; the LLM choice is reset so you pick your own. The forked character will report incomplete until you fill in llm_provider/llm_model/llm_temperature.
This record as markdown: /tools/flowdot-llc-mcp-server/fork-agent-character.md
What fork_agent_character does on FlowDot MCP Server
AI agents use fork_agent_character to create or update resources in FlowDot MCP Server, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your FlowDot MCP Server environment.
Why fork_agent_character is rated Medium
This tool creates a new copy of a public agent character in the user's library. It is a reversible write operation (creating a new resource), not destructive, not financial, and not executing arbitrary code. The blast radius is low as it only adds a derived resource to the user's own library.
From the tool's definition Fork a public agent character to your library by hash. Voice config + persona are copied; the LLM choice is reset so you pick your own.
Attacks that exploit this kind of access
The rule that runs fork_agent_character safely
PolicyLayer is an MCP gateway: it sits between your AI agents and FlowDot MCP Server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For fork_agent_character, this is the rule to start with:
fork_agent_character 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.
The button opens the PolicyLayer dashboard: create your workspace, connect FlowDot MCP Server, apply this rule, and every fork_agent_character call is checked against it from then on.
Questions about fork_agent_character
Fork a public agent character to your library by hash. Voice config + persona are copied; the LLM choice is reset so you pick your own. The forked character will report incomplete until you fill in llm_provider/llm_model/llm_temperature. It is categorised as a Write tool in the FlowDot MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the FlowDot MCP Server MCP server in PolicyLayer and add a rule for fork_agent_character: 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 FlowDot MCP Server. Nothing to install.
fork_agent_character 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 fork_agent_character 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 fork_agent_character. 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.
fork_agent_character is provided by the FlowDot MCP Server MCP server (flowdot-llc/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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