Additive, non-destructive operations. Supported operations: create_element, import_element, addEntry, verify_challenge, release_deadlock, beetlejuice_beetlejuice_beetlejuice, record_execution_step, install_collection_content, submit_collection_content, init_portfolio, sync_portfolio, portfolio_el...
Part of the DollhouseMCP server.
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AI agents use mcp_aql_create to create or modify resources in DollhouseMCP. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call mcp_aql_create repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach DollhouseMCP.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"mcp_aql_create": {
"limits": [
{
"counter": "mcp_aql_create_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full DollhouseMCP policy for all 5 tools.
These attack patterns abuse exactly the kind of access mcp_aql_create gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Additive, non-destructive operations. Supported operations: create_element, import_element, addEntry, verify_challenge, release_deadlock, beetlejuice_beetlejuice_beetlejuice, record_execution_step, install_collection_content, submit_collection_content, init_portfolio, sync_portfolio, portfolio_element_manager, setup_github_auth, configure_oauth, import_persona Element types: persona, skill, template, agent, memory, ensemble These operations add new data without removing or overwriting existing content. Quick start examples: { operation: "create_element", element_type: "persona", params: { element_name: "MyPersona", description: "A helpful assistant", instructions: "You ARE a helpful assistant. ALWAYS provide clear, accurate responses." } } { operation: "create_element", element_type: "agent", params: { element_name: "MyAgent", description: "Task executor", instructions: "Execute goals methodically. Report progress at each step.", goal: { template: "Complete: {objective}", parameters: [{ name: "objective", type: "string", required: true }] } } } { operation: "create_element", element_type: "memory", params: { element_name: "session-notes", description: "Session context and notes" } } { operation: "create_element", element_type: "ensemble", params: { element_name: "my-ensemble", description: "Combined element set", metadata: { elements: [{ element_name: "expert", element_type: "persona", role: "primary" }, { element_name: "analysis", element_type: "skill", role: "support" }] } } } Valid ensemble roles: primary, support, override, monitor, core { operation: "addEntry", params: { element_name: "session-notes", content: "Remember this fact", tags: ["important"] } } Note: addEntry content supports markdown (headers, lists, bold, tables, code blocks). Ensure markdown content is properly JSON-escaped — use \n for newlines, \" for quotes, and \\ for backslashes within the JSON string value. Execution lifecycle — record agent progress (appends step records, like addEntry): { operation: "record_execution_step", params: { element_name: "code-reviewer", stepDescription: "Analyzed files", outcome: "success", findings: "Found 3 issues" } } This is the normal next lifecycle call after mcp_aql_execute { operation: "execute_agent", ... }. Response flow: record_execution_step returns { autonomy: { continue, factors, notifications? } }. Check autonomy.continue to decide whether to proceed. Check autonomy.notifications for permission_pending (gatekeeper blocks), autonomy_pause, or danger_zone alerts to relay to human operators. Import & portfolio: { operation: "import_element", element_type: "skill", params: { element_name: "code-formatter", data: "..." } } { operation: "import_persona", params: { source: "/path/to/persona.md" } } { operation: "install_collection_content", params: { element_type: "persona", element_name: "Creative-Writer" } } { operation: "submit_collection_content", params: { element_type: "skill", element_name: "code-formatter" } } { operation: "init_portfolio" } { operation: "sync_portfolio" } { operation: "portfolio_element_manager", params: { action: "push", element_type: "persona", element_name: "Tech-Writer" } } Auth & verification: { operation: "setup_github_auth" } { operation: "configure_oauth", params: { client_id: "your-client-id" } } { operation: "verify_challenge", params: { code: "ABC123" } } { operation: "release_deadlock" } { operation: "beetlejuice_beetlejuice_beetlejuice" } Batch operations: Use the operations array to execute multiple operations sequentially in a single request. { operations: [{ operation: "addEntry", params: { element_name: "log", content: "Step 1" } }, { operation: "addEntry", params: { element_name: "log", content: "Step 2" } }] } Discover required parameters — use mcp_aql_read: { operation: "introspect", params: { query: "operations", name: "create_element" } } Discover element format specs (required fields, syntax, examples) — use mcp_aql_read: { operation: "introspect", params: { query: "format", name: "template" } }. It is categorised as a Write tool in the DollhouseMCP MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Dollhouse MCP server in PolicyLayer and add a rule for mcp_aql_create: 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 DollhouseMCP. Nothing to install.
mcp_aql_create 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 mcp_aql_create 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 mcp_aql_create. 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.
mcp_aql_create is provided by the Dollhouse MCP server (@dollhousemcp/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 5 DollhouseMCP tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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