Execution lifecycle operations for executable elements (agents, workflows, pipelines). Supported operations: confirm_operation, approve_cli_permission, execute_agent, complete_execution, continue_execution, abort_execution, prepare_handoff, resume_from_handoff These operations manage runtime ex...
Part of the DollhouseMCP MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.
AI agents invoke mcp_aql_execute to trigger processes or run actions in DollhouseMCP. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
mcp_aql_execute can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. Intercept enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
tools:
mcp_aql_execute:
rules:
- action: allow
rate_limit:
max: 10
window: 60
validate:
required_args: trueSee the full DollhouseMCP policy for all 5 tools.
Agents calling execute-class tools like mcp_aql_execute have been implicated in these attack patterns. Read the full case and prevention policy for each:
Other tools in the Execute risk category across the catalogue. The same policy patterns (rate-limit, validate) apply to each.
mcp_aql_execute is one of the high-risk operations in DollhouseMCP. For the full severity-focused view — only the high-risk tools with their recommended policies — see the breakdown for this server, or browse all high-risk tools across every MCP server.
Execution lifecycle operations for executable elements (agents, workflows, pipelines). Supported operations: confirm_operation, approve_cli_permission, execute_agent, complete_execution, continue_execution, abort_execution, prepare_handoff, resume_from_handoff These operations manage runtime execution state. Unlike CRUD operations (which manage definitions), Execute operations handle the execution lifecycle: - execute_agent: Start a new execution (returns goalId and stateVersion for tracking) - complete_execution: Signal successful completion - continue_execution: Resume from saved state - abort_execution: Abort a running execution, rejecting further operations - confirm_operation: Confirm a pending operation that requires user approval (Gatekeeper flow) - approve_cli_permission: Approve a pending CLI tool permission request - prepare_handoff: Serialize goal progress into a portable handoff block for session transfer - resume_from_handoff: Resume agent execution from a handoff block with integrity validation IMPORTANT: Execute operations are potentially destructive (agents can perform any action) and non-idempotent (calling execute_agent twice creates two separate executions). ⚠️ SECURITY: Do not auto-allow this endpoint in your host settings (e.g., Claude Code settings.json). Each execution should require explicit human approval. Auto-allowing bypasses the per-operation confirmation gate. While DangerZone verification and element deny policies still provide protection, the primary human review checkpoint is lost. Response flow: execute_agent returns { goalId, stateVersion, activeElements, safetyTier, ... }. Use goalId with record_execution_step and complete_execution. stateVersion enables optimistic locking. record_execution_step returns { autonomy: { continue, notifications? } } — check notifications for gatekeeper blocks and danger zone alerts. Quick start examples: { operation: "execute_agent", params: { element_name: "code-reviewer", parameters: { objective: "Review code" } } } { operation: "complete_execution", params: { element_name: "code-reviewer", outcome: "success", summary: "Completed review" } } { operation: "abort_execution", params: { element_name: "data-collector", reason: "User requested cancellation" } } { operation: "continue_execution", params: { element_name: "code-reviewer" } } { operation: "confirm_operation", params: { operation: "execute_agent" } } { operation: "approve_cli_permission", params: { request_id: "req-123", decision: "allow" } } { operation: "prepare_handoff", params: { element_name: "code-reviewer" } } { operation: "resume_from_handoff", params: { element_name: "code-reviewer", handoff_block: "..." } } Discover required parameters — use mcp_aql_read: { operation: "introspect", params: { query: "operations", name: "execute_agent" } }. It is categorised as a Execute tool in the DollhouseMCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Add a rule in your Intercept YAML policy under the tools section for mcp_aql_execute. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the DollhouseMCP MCP server.
mcp_aql_execute is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the mcp_aql_execute rule in your Intercept 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 Intercept policy for mcp_aql_execute. 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_execute is provided by the DollhouseMCP MCP server (@dollhousemcp/mcp-server). Intercept sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Open source. One binary. Zero dependencies.
npx -y @policylayer/intercept