ClaudeFlow ported, needs cleanup
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Feature: MCP (Model Context Protocol) Integration
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As a CleverClaude user
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I want to use MCP tools and services
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So that I can extend CleverClaude capabilities with external tools
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Background:
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Given CleverClaude is running
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And the MCP client is initialized
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@smoke
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Scenario: Initialize MCP client
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When I initialize the MCP client
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Then the MCP client should be ready
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And available tools should be loaded
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And the client should be connected to MCP servers
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Scenario: List available MCP tools
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When I request the list of available MCP tools
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Then I should receive a list of tools
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And the list should contain more than 80 tools
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And each tool should have proper metadata
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Scenario Outline: Execute basic MCP tools
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When I execute the MCP tool "<tool_name>" with parameters:
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"""
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<parameters>
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"""
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Then the tool should execute successfully
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And I should receive valid results
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And the response should match the expected format
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Examples:
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| tool_name | parameters |
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| swarm_init | {"topology": "mesh", "maxAgents": 5} |
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| agent_spawn | {"type": "researcher", "name": "test_agent"} |
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| task_orchestrate | {"task": "Simple test task"} |
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| swarm_status | {} |
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| memory_usage | {"action": "list"} |
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Scenario: Execute swarm management via MCP
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When I execute MCP tool "swarm_init" with parameters:
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"""
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{
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"topology": "hierarchical",
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"maxAgents": 10,
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"strategy": "balanced"
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}
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"""
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Then a new swarm should be created
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And the swarm should have hierarchical topology
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When I execute MCP tool "agent_spawn" with parameters:
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"""
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{
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"type": "researcher",
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"name": "mcp_researcher",
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"capabilities": ["research", "analysis"]
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}
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"""
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Then a new agent should be spawned
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And the agent should be added to the swarm
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Scenario: Neural network operations via MCP
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When I execute MCP tool "neural_train" with parameters:
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"""
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{
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"pattern_type": "coordination",
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"training_data": "sample training data",
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"epochs": 10
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}
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"""
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Then neural training should begin
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And training progress should be reported
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When I execute MCP tool "neural_predict" with parameters:
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"""
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{
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"modelId": "coordination_model",
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"input": "test prediction input"
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}
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"""
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Then prediction results should be returned
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Scenario: Memory management via MCP
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When I execute MCP tool "memory_usage" with parameters:
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"""
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{
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"action": "store",
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"key": "test_key",
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"value": "test_value",
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"namespace": "test_namespace"
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}
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"""
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Then the data should be stored successfully
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When I execute MCP tool "memory_usage" with parameters:
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"""
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{
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"action": "retrieve",
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"key": "test_key",
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"namespace": "test_namespace"
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}
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"""
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Then the stored data should be retrieved
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And the retrieved value should match "test_value"
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Scenario: Performance monitoring via MCP
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Given I have an active swarm with agents
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When I execute MCP tool "performance_report" with parameters:
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"""
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{
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"format": "detailed",
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"timeframe": "24h"
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}
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"""
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Then I should receive detailed performance metrics
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And metrics should include swarm statistics
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And metrics should include agent performance data
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Scenario: Workflow automation via MCP
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When I execute MCP tool "workflow_create" with parameters:
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"""
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{
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"name": "test_workflow",
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"steps": [
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{"action": "create_agent", "type": "researcher"},
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{"action": "assign_task", "task_type": "analysis"},
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{"action": "collect_results"}
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]
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}
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"""
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Then a new workflow should be created
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When I execute MCP tool "workflow_execute" with parameters:
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"""
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{
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"workflowId": "test_workflow"
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}
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"""
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Then the workflow should execute successfully
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And all workflow steps should complete
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Scenario: Error handling in MCP operations
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When I execute MCP tool "swarm_init" with invalid parameters:
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"""
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{
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"topology": "invalid_topology",
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"maxAgents": -1
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}
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"""
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Then the operation should fail gracefully
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And I should receive a meaningful error message
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And the system should remain stable
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Scenario: MCP tool discovery and metadata
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When I request tool metadata for "agent_spawn"
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Then I should receive complete tool information
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And the metadata should include parameter schemas
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And the metadata should include usage examples
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And the metadata should specify return types
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@wip
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Scenario: Custom MCP server integration
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Given I have a custom MCP server running
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When I register the custom server with CleverClaude
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Then the server should be added to available servers
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And custom tools should be discoverable
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And I should be able to execute custom tools
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Scenario: Concurrent MCP operations
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When I execute multiple MCP tools simultaneously:
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| tool_name | parameters |
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| swarm_status | {} |
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| agent_metrics | {"agentId": "test_agent"} |
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| memory_usage | {"action": "list"} |
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| performance_report | {"format": "summary"} |
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Then all operations should complete successfully
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And no operation should block others
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And results should be returned in reasonable time
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Scenario: MCP session management
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When I start a new MCP session
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Then session state should be initialized
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When I execute multiple related operations in the session
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Then session context should be maintained
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And operations should share session state
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When I close the MCP session
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Then all session resources should be cleaned up
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@hypothesis
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Scenario: Stress test MCP operations
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When I execute many MCP operations rapidly
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Then all operations should complete or fail gracefully
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And the MCP client should remain responsive
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And no memory leaks should occur
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And connection pools should be managed properly
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Scenario: MCP connection resilience
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Given I have an active MCP connection
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When the MCP server becomes temporarily unavailable
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Then the client should detect the connection loss
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And automatic reconnection should be attempted
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When the server becomes available again
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Then the connection should be restored
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And pending operations should resume
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Scenario: MCP tool versioning and compatibility
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When I request tool version information
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Then I should receive version details for each tool
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And compatibility information should be provided
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When I execute a tool with version-specific parameters
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Then the correct tool version should be used
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And deprecated features should show warnings
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