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