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aywengo

MCP Kafka Schema Reg

guided_disaster_recovery

Configure disaster recovery strategies for Kafka Schema Registry by initiating the setup workflow to ensure data availability and resilience.

Instructions

Start the Disaster Recovery Setup workflow for configuring DR strategies

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The primary MCP tool handler for 'guided_disaster_recovery'. This async function is decorated with @self.mcp.tool and initiates the multi-step 'disaster_recovery_setup' workflow, returning a JSON response with workflow status and first step details.
    @self.mcp.tool(description="Start the Disaster Recovery Setup workflow for configuring DR strategies")
    async def guided_disaster_recovery() -> str:
        """Convenience method to start Disaster Recovery workflow."""
        workflow_id = "disaster_recovery_setup"
    
        try:
            request = await self.multi_step_manager.start_workflow(workflow_id=workflow_id, initial_context={})
    
            if request:
                return json.dumps(
                    {
                        "status": "started",
                        "workflow_id": workflow_id,
                        "workflow_name": "Disaster Recovery Setup",
                        "request_id": request.id,
                        "first_step": request.title,
                        "description": request.description,
                        "message": (
                            "Disaster Recovery Setup workflow started. This workflow will guide you through:\n"
                            "1. Current infrastructure assessment\n"
                            "2. Backup strategy configuration\n"
                            "3. Recovery procedures planning\n"
                            "4. Testing and validation setup"
                        ),
                    }
                )
            else:
                return json.dumps({"error": "Failed to start Disaster Recovery workflow"})
        except Exception as e:
            logger.error(f"Error starting Disaster Recovery workflow: {str(e)}")
            return json.dumps({"error": f"Failed to start workflow: {str(e)}"})
  • Supporting helper function that takes the completed workflow responses and constructs a detailed disaster recovery execution plan/strategy based on user inputs during the guided workflow.
    def execute_disaster_recovery_setup(responses: Dict[str, Any]) -> Dict[str, Any]:
        """Execute disaster recovery setup based on workflow responses."""
        dr_strategy = responses.get("dr_strategy")
    
        result = {"operation": "disaster_recovery_setup", "strategy": dr_strategy, "status": "pending"}
    
        # Add strategy-specific configuration
        if dr_strategy == "active_passive":
            result["config"] = {
                "primary_registry": responses.get("primary_registry"),
                "standby_registry": responses.get("standby_registry"),
                "replication_interval": responses.get("replication_interval"),
                "failover_mode": responses.get("failover_mode"),
            }
        elif dr_strategy == "active_active":
            result["config"] = {
                "active_registries": [reg.strip() for reg in responses.get("active_registries", "").split(",")],
                "conflict_resolution": responses.get("conflict_resolution"),
                "sync_topology": responses.get("sync_topology"),
            }
        elif dr_strategy == "backup_restore":
            result["config"] = {
                "backup_schedule": responses.get("backup_schedule"),
                "backup_location": responses.get("backup_location"),
                "retention_policy": responses.get("retention_policy"),
                "encryption": responses.get("encryption") == "true",
            }
        elif dr_strategy == "multi_region":
            result["config"] = {
                "regions": [region.strip() for region in responses.get("regions", "").split(",")],
                "primary_region": responses.get("primary_region"),
                "data_residency": responses.get("data_residency") == "true",
                "cross_region_replication": responses.get("cross_region_replication"),
            }
    
        # Add common DR options
        result["options"] = {
            "enable_monitoring": responses.get("enable_monitoring") == "true",
            "run_dr_drill": responses.get("run_dr_drill") == "true",
            "generate_runbook": responses.get("generate_runbook") == "true",
            "initial_sync": responses.get("initial_sync") == "true",
        }
    
        return result
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden but only states it starts a workflow without detailing what that entails (e.g., interactive steps, side effects, permissions required, or time commitment). It mentions 'configuring DR strategies' but doesn't explain the behavioral impact or expected outcomes.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core action ('Start the Disaster Recovery Setup workflow') and adds purpose ('for configuring DR strategies'). Every word contributes value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 0 parameters, no annotations, and an output schema (implied by context signals), the description is minimally adequate but lacks details on workflow behavior, outcomes, or interaction patterns. It covers the purpose but misses context like what the workflow does or how it differs from other guided tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters with 100% schema coverage, so no parameter documentation is needed. The description doesn't add param info, which is appropriate, but it could hint at implicit inputs (e.g., user context), though not required. Baseline is 4 for zero-param tools.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Start') and the target ('Disaster Recovery Setup workflow'), specifying it's for configuring DR strategies. It distinguishes from siblings like 'start_workflow' by being more specific about the workflow type, though it doesn't explicitly contrast with all similar tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like 'start_workflow' or other guided workflows (e.g., 'guided_context_reorganization'). The description implies it's for DR setup but lacks context on prerequisites, timing, or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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