MCP Dummy DB Integration

2026-01-24 16:34:09,448 - agents.orchestrator - INFO - 🚀 Initializing Orchestrator with ollama (tinyllama) 2026-01-24 16:34:09,448 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:34:09,451 - agents.orchestrator - INFO - 🎯 Processing Query: Show me all employees in the AI department 2026-01-24 16:34:09,451 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:34:09,452 - agents.orchestrator - INFO - [1/3] 🧠 Planning... 2026-01-24 16:34:09,452 - agents.planner_agent - INFO - 🧠 Planner analyzing: Show me all employees in the AI department 2026-01-24 16:34:09,453 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:34:30,948 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:34:30,951 - agents.planner_agent - INFO - LLM Response: To decide which MCP tool to call based on the provided user query, we can follow these steps: 1. Extract the parameter value from the user query by splitting it on whitespace and using the `split()` f... 2026-01-24 16:34:30,952 - agents.planner_agent - ERROR - JSON parsing error: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) 2026-01-24 16:34:30,952 - agents.orchestrator - ERROR - Planning error: JSON parsing error: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) 2026-01-24 16:34:30,952 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:34:30,952 - agents.orchestrator - INFO - 🎯 Processing Query: What are the projects that are in progress? 2026-01-24 16:34:30,952 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:34:30,952 - agents.orchestrator - INFO - [1/3] 🧠 Planning... 2026-01-24 16:34:30,952 - agents.planner_agent - INFO - 🧠 Planner analyzing: What are the projects that are in progress? 2026-01-24 16:34:30,957 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:35:06,528 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:35:06,533 - agents.planner_agent - INFO - LLM Response: To use the available tools, you need to extract the parameters from the user query and send them only with valid JSON. Here are some examples of how to request each tool with its corresponding parame... 2026-01-24 16:35:06,533 - agents.planner_agent - ERROR - JSON parsing error: Extra data: line 8 column 1 (char 191) 2026-01-24 16:35:06,535 - agents.orchestrator - ERROR - Planning error: JSON parsing error: Extra data: line 8 column 1 (char 191) 2026-01-24 16:35:06,537 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:35:06,537 - agents.orchestrator - INFO - 🎯 Processing Query: Get me all high priority issues 2026-01-24 16:35:06,538 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:35:06,538 - agents.orchestrator - INFO - [1/3] 🧠 Planning... 2026-01-24 16:35:06,539 - agents.planner_agent - INFO - 🧠 Planner analyzing: Get me all high priority issues 2026-01-24 16:35:06,539 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:35:48,564 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:35:48,564 - agents.planner_agent - INFO - LLM Response: To use the available tools for the user query "Get me all high priority issues", we would follow these steps: 1. Identify which MCP tool is relevant to the user's request based on its description and... 2026-01-24 16:35:48,565 - agents.planner_agent - ERROR - No JSON found in response 2026-01-24 16:35:48,566 - agents.orchestrator - ERROR - Planning error: Could not parse LLM response 2026-01-24 16:35:48,566 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:35:48,566 - agents.orchestrator - INFO - 🎯 Processing Query: Find all completed projects 2026-01-24 16:35:48,568 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:35:48,568 - agents.orchestrator - INFO - [1/3] 🧠 Planning... 2026-01-24 16:35:48,569 - agents.planner_agent - INFO - 🧠 Planner analyzing: Find all completed projects 2026-01-24 16:35:48,569 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:36:22,997 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:36:22,999 - agents.planner_agent - INFO - LLM Response: To respond to the user query, "Find all completed projects", the following MCP tool is recommended: 1. Get Employees by Department (available tools: `mcp_get_employees_by_department`) Parameters: - ... 2026-01-24 16:36:22,999 - agents.planner_agent - ERROR - No JSON found in response 2026-01-24 16:36:23,001 - agents.orchestrator - ERROR - Planning error: Could not parse LLM response 2026-01-24 16:36:23,003 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:36:23,004 - agents.orchestrator - INFO - 🎯 Processing Query: Show employees from the backend team 2026-01-24 16:36:23,005 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:36:23,005 - agents.orchestrator - INFO - [1/3] 🧠 Planning... 2026-01-24 16:36:23,005 - agents.planner_agent - INFO - 🧠 Planner analyzing: Show employees from the backend team 2026-01-24 16:36:23,007 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:37:20,905 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:37:20,908 - agents.planner_agent - INFO - LLM Response: To decide which MCP tool to call for the provided user query, we need to extract the parameter value from the user query. Here's an example of how to do that: Assuming that the user query is "Show em... 2026-01-24 16:37:20,909 - agents.planner_agent - ERROR - JSON parsing error: Extra data: line 5 column 4 (char 107) 2026-01-24 16:37:20,909 - agents.orchestrator - ERROR - Planning error: JSON parsing error: Extra data: line 5 column 4 (char 107) 2026-01-24 16:39:14,499 - agents.orchestrator - INFO - 🚀 Initializing Orchestrator with ollama (tinyllama) 2026-01-24 16:39:14,499 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:39:14,499 - agents.orchestrator - INFO - 🎯 Processing Query: Show me all employees in the AI department 2026-01-24 16:39:14,499 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:39:14,501 - agents.orchestrator - INFO - [1/3] 🧠 Planning... 2026-01-24 16:39:14,501 - agents.planner_agent - INFO - 🧠 Planner analyzing: Show me all employees in the AI department 2026-01-24 16:39:14,501 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:39:28,591 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:39:28,593 - agents.planner_agent - INFO - LLM Response: According to the provided user query, "Show me all employees in the AI department", we would use the "mcp_get_employees_by_department" tool to retrieve this information. Here's how it would look: ```... 2026-01-24 16:39:28,593 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'Since you asked for employees in the AI department, we selected the mcp_get_employees_by_department tool.'} 2026-01-24 16:39:28,594 - agents.orchestrator - INFO - [2/3] ⚙️ Executing... 2026-01-24 16:39:28,594 - agents.executor_agent - INFO - ⚙️ Executing plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'Since you asked for employees in the AI department, we selected the mcp_get_employees_by_department tool.'} 2026-01-24 16:39:28,595 - agents.executor_agent - INFO - Tool: mcp_get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 16:39:28,595 - agents.executor_agent - INFO - Calling tool: mcp_get_employees_by_department 2026-01-24 16:39:28,595 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 16:39:28,713 - database.db_executor - INFO - Connected to database successfully 2026-01-24 16:39:28,724 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 16:39:28,725 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 16:39:28,725 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 16:39:28,726 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 16:39:28,726 - agents.orchestrator - ERROR - ❌ Orchestrator error: Object of type Decimal is not JSON serializable 2026-01-24 16:39:28,727 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:39:28,728 - agents.orchestrator - INFO - 🎯 Processing Query: What are the projects that are in progress? 2026-01-24 16:39:28,728 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:39:28,729 - agents.orchestrator - INFO - [1/3] 🧠 Planning... 2026-01-24 16:39:28,729 - agents.planner_agent - INFO - 🧠 Planner analyzing: What are the projects that are in progress? 2026-01-24 16:39:28,729 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:39:57,767 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:39:57,767 - agents.planner_agent - INFO - LLM Response: Based on the user query "What are the projects that are in progress?" and its available tools, we can use the "mcp_get_projects_by_status" tool to retrieve a list of projects with a status of "Complet... 2026-01-24 16:39:57,769 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_projects_by_status', 'parameters': {'value': 'on hold'}, 'reasoning': "Returns all projects that are currently on hold and have a status of 'On Hold'"} 2026-01-24 16:39:57,770 - agents.orchestrator - INFO - [2/3] ⚙️ Executing... 2026-01-24 16:39:57,770 - agents.executor_agent - INFO - ⚙️ Executing plan: {'tool': 'mcp_get_projects_by_status', 'parameters': {'value': 'on hold'}, 'reasoning': "Returns all projects that are currently on hold and have a status of 'On Hold'"} 2026-01-24 16:39:57,770 - agents.executor_agent - INFO - Tool: mcp_get_projects_by_status, Parameters: {'value': 'on hold'} 2026-01-24 16:39:57,770 - agents.executor_agent - INFO - Calling tool: mcp_get_projects_by_status 2026-01-24 16:39:57,770 - mcp.tools - INFO - Tool called: get_projects_by_status with input=on hold 2026-01-24 16:39:57,863 - database.db_executor - INFO - Connected to database successfully 2026-01-24 16:39:57,863 - database.db_executor - INFO - Found 0 projects with status on hold 2026-01-24 16:39:57,863 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 0 rows 2026-01-24 16:39:57,863 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 16:39:57,863 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 16:39:57,863 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:40:21,024 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:40:21,025 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 16:40:21,025 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:40:21,026 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 16:40:21,026 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:40:21,026 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:40:21,026 - agents.orchestrator - INFO - 🎯 Processing Query: Get me all high priority issues 2026-01-24 16:40:21,026 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:40:21,026 - agents.orchestrator - INFO - [1/3] 🧠 Planning... 2026-01-24 16:40:21,026 - agents.planner_agent - INFO - 🧠 Planner analyzing: Get me all high priority issues 2026-01-24 16:40:21,030 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:40:50,643 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:40:50,643 - agents.planner_agent - INFO - LLM Response: Based on the provided user query and available tools, the "mcp_get_issues_by_priority" tool should be called as follows: Request: { "tool": "mcp_get_issues_by_priority", "parameters": {"priorit... 2026-01-24 16:40:50,645 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_issues_by_priority', 'parameters': {'priority': 'High'}, 'reasoning': 'Use this tool to retrieve issues with a priority level of High'} 2026-01-24 16:40:50,645 - agents.orchestrator - INFO - [2/3] ⚙️ Executing... 2026-01-24 16:40:50,645 - agents.executor_agent - INFO - ⚙️ Executing plan: {'tool': 'mcp_get_issues_by_priority', 'parameters': {'priority': 'High'}, 'reasoning': 'Use this tool to retrieve issues with a priority level of High'} 2026-01-24 16:40:50,645 - agents.executor_agent - INFO - Tool: mcp_get_issues_by_priority, Parameters: {'priority': 'High'} 2026-01-24 16:40:50,645 - agents.executor_agent - INFO - Calling tool: mcp_get_issues_by_priority 2026-01-24 16:40:50,645 - mcp.tools - INFO - Tool called: get_issues_by_priority with input=High 2026-01-24 16:40:50,735 - database.db_executor - INFO - Connected to database successfully 2026-01-24 16:40:50,738 - database.db_executor - INFO - Found 2 issues with priority High 2026-01-24 16:40:50,738 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 2 rows 2026-01-24 16:40:50,738 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 16:40:50,738 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 16:40:50,738 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:41:18,654 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:41:18,655 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 16:41:18,655 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:41:18,656 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 16:41:18,656 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:41:18,659 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:41:18,659 - agents.orchestrator - INFO - 🎯 Processing Query: Find all completed projects 2026-01-24 16:41:18,659 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:41:18,661 - agents.orchestrator - INFO - [1/3] 🧠 Planning... 2026-01-24 16:41:18,661 - agents.planner_agent - INFO - 🧠 Planner analyzing: Find all completed projects 2026-01-24 16:41:18,661 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:41:46,138 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:41:46,142 - agents.planner_agent - INFO - LLM Response: Based on the provided tools and user query, we recommend calling the "mcp_get_issues_by_priority" tool to retrieve completed projects. Here's how it could look like in response format: { "tool": "... 2026-01-24 16:41:46,146 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'tool_name', 'parameters': {'priority': 'High'}, 'reasoning': "The reason we recommend using the 'mcp_get_issues_by_priority' tool is because it allows us to filter completed projects based on their priority level (i.e., High)."} 2026-01-24 16:41:46,151 - agents.orchestrator - INFO - [2/3] ⚙️ Executing... 2026-01-24 16:41:46,156 - agents.executor_agent - INFO - ⚙️ Executing plan: {'tool': 'tool_name', 'parameters': {'priority': 'High'}, 'reasoning': "The reason we recommend using the 'mcp_get_issues_by_priority' tool is because it allows us to filter completed projects based on their priority level (i.e., High)."} 2026-01-24 16:41:46,159 - agents.executor_agent - INFO - Tool: tool_name, Parameters: {'priority': 'High'} 2026-01-24 16:41:46,160 - agents.executor_agent - ERROR - ❌ Tool not found: tool_name 2026-01-24 16:41:46,161 - agents.orchestrator - ERROR - Execution error: Tool 'tool_name' not allowed 2026-01-24 16:41:46,163 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:41:46,164 - agents.orchestrator - INFO - 🎯 Processing Query: Show employees from the backend team 2026-01-24 16:41:46,166 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:41:46,167 - agents.orchestrator - INFO - [1/3] 🧠 Planning... 2026-01-24 16:41:46,169 - agents.planner_agent - INFO - 🧠 Planner analyzing: Show employees from the backend team 2026-01-24 16:41:46,169 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:42:14,009 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:42:14,014 - agents.planner_agent - INFO - LLM Response: Based on the provided set of query parameters, the recommended MCP tool for analyzing user queries and deciding which tool to call is "Tool name". Here's how to use it: 1. Extract the parameter value... 2026-01-24 16:42:14,022 - agents.planner_agent - ERROR - JSON parsing error: Expecting property name enclosed in double quotes: line 3 column 46 (char 74) 2026-01-24 16:42:14,034 - agents.orchestrator - ERROR - Planning error: JSON parsing error: Expecting property name enclosed in double quotes: line 3 column 46 (char 74) 2026-01-24 16:42:33,579 - agents.orchestrator - INFO - 🚀 Initializing Orchestrator with ollama (tinyllama) 2026-01-24 16:42:33,579 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:42:33,580 - agents.orchestrator - INFO - 🎯 Processing Query: Show me all employees in the AI department 2026-01-24 16:42:33,581 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:42:33,581 - agents.orchestrator - INFO - [1/3] 🧠 Planning... 2026-01-24 16:42:33,581 - agents.planner_agent - INFO - 🧠 Planner analyzing: Show me all employees in the AI department 2026-01-24 16:42:33,581 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:42:58,567 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:42:58,567 - agents.planner_agent - INFO - LLM Response: To choose a specific MCP tool based on the user's query, we should extract the parameter value and use it as input to that specific tool. For example: 1. If the user asks for employees in the AI depa... 2026-01-24 16:42:58,568 - agents.planner_agent - ERROR - JSON parsing error: Extra data: line 8 column 1 (char 163) 2026-01-24 16:42:58,568 - agents.orchestrator - ERROR - Planning error: JSON parsing error: Extra data: line 8 column 1 (char 163) 2026-01-24 16:42:58,568 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:42:58,568 - agents.orchestrator - INFO - 🎯 Processing Query: What are the projects that are in progress? 2026-01-24 16:42:58,568 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:42:58,568 - agents.orchestrator - INFO - [1/3] 🧠 Planning... 2026-01-24 16:42:58,568 - agents.planner_agent - INFO - 🧠 Planner analyzing: What are the projects that are in progress? 2026-01-24 16:42:58,568 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:43:12,396 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:43:12,396 - agents.planner_agent - INFO - LLM Response: To respond to the user's query "What are the projects that are in progress?", the MCP tool must be able to filter projects based on their status and priorities. The following is an example response fo... 2026-01-24 16:43:12,397 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_issues_by_priority', 'parameters': {'priority': 'High'}, 'reasoning': "MCP tool supports filtering projects based on priority level, which is specified as 'High' in this request."} 2026-01-24 16:43:12,397 - agents.orchestrator - INFO - [2/3] ⚙️ Executing... 2026-01-24 16:43:12,397 - agents.executor_agent - INFO - ⚙️ Executing plan: {'tool': 'mcp_get_issues_by_priority', 'parameters': {'priority': 'High'}, 'reasoning': "MCP tool supports filtering projects based on priority level, which is specified as 'High' in this request."} 2026-01-24 16:43:12,397 - agents.executor_agent - INFO - Tool: mcp_get_issues_by_priority, Parameters: {'priority': 'High'} 2026-01-24 16:43:12,397 - agents.executor_agent - INFO - Calling tool: mcp_get_issues_by_priority 2026-01-24 16:43:12,397 - mcp.tools - INFO - Tool called: get_issues_by_priority with input=High 2026-01-24 16:43:12,484 - database.db_executor - INFO - Connected to database successfully 2026-01-24 16:43:12,489 - database.db_executor - INFO - Found 2 issues with priority High 2026-01-24 16:43:12,489 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 2 rows 2026-01-24 16:43:12,489 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 16:43:12,490 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 16:43:12,490 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:43:36,292 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:43:36,292 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 16:43:36,293 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:43:36,293 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 16:43:36,293 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:43:36,293 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:43:36,293 - agents.orchestrator - INFO - 🎯 Processing Query: Get me all high priority issues 2026-01-24 16:43:36,293 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:43:36,293 - agents.orchestrator - INFO - [1/3] 🧠 Planning... 2026-01-24 16:43:36,293 - agents.planner_agent - INFO - 🧠 Planner analyzing: Get me all high priority issues 2026-01-24 16:43:36,293 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:44:22,959 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:44:22,960 - agents.planner_agent - INFO - LLM Response: As a database query planning assistant, I would choose the "mcp_get_issues_by_priority" tool to be called. The user query is: "Get me all high priority issues", which can be extracted into a parameter... 2026-01-24 16:44:22,960 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_issues_by_priority', 'parameters': {'department': 'value', 'status': 'value', 'prorytiy': ['High', False, None]}, 'reasoning': "Valid request, the 'pristry' parameter needs to be updated to handle 'high priority'."} 2026-01-24 16:44:22,961 - agents.orchestrator - INFO - [2/3] ⚙️ Executing... 2026-01-24 16:44:22,961 - agents.executor_agent - INFO - ⚙️ Executing plan: {'tool': 'mcp_get_issues_by_priority', 'parameters': {'department': 'value', 'status': 'value', 'prorytiy': ['High', False, None]}, 'reasoning': "Valid request, the 'pristry' parameter needs to be updated to handle 'high priority'."} 2026-01-24 16:44:22,961 - agents.executor_agent - INFO - Tool: mcp_get_issues_by_priority, Parameters: {'department': 'value', 'status': 'value', 'prorytiy': ['High', False, None]} 2026-01-24 16:44:22,961 - agents.executor_agent - INFO - Calling tool: mcp_get_issues_by_priority 2026-01-24 16:44:22,962 - mcp.tools - INFO - Tool called: get_issues_by_priority with input=value 2026-01-24 16:44:23,029 - database.db_executor - INFO - Connected to database successfully 2026-01-24 16:44:23,030 - database.db_executor - INFO - Found 0 issues with priority value 2026-01-24 16:44:23,030 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 0 rows 2026-01-24 16:44:23,030 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 16:44:23,030 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 16:44:23,030 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:44:35,600 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:44:35,600 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 16:44:35,601 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:44:35,601 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 16:44:35,601 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:44:35,602 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:44:35,602 - agents.orchestrator - INFO - 🎯 Processing Query: Find all completed projects 2026-01-24 16:44:35,602 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:44:35,602 - agents.orchestrator - INFO - [1/3] 🧠 Planning... 2026-01-24 16:44:35,602 - agents.planner_agent - INFO - 🧠 Planner analyzing: Find all completed projects 2026-01-24 16:44:35,602 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:45:13,798 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:45:13,799 - agents.planner_agent - INFO - LLM Response: To use the available tools in this scenario, we would need to extract the relevant parameters from the user query using regular expressions. Here's an example: For the "get_projects_by_status" tool, ... 2026-01-24 16:45:13,800 - agents.planner_agent - ERROR - JSON parsing error: Extra data: line 9 column 1 (char 188) 2026-01-24 16:45:13,800 - agents.orchestrator - ERROR - Planning error: JSON parsing error: Extra data: line 9 column 1 (char 188) 2026-01-24 16:45:13,801 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:45:13,802 - agents.orchestrator - INFO - 🎯 Processing Query: Show employees from the backend team 2026-01-24 16:45:13,802 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:45:13,803 - agents.orchestrator - INFO - [1/3] 🧠 Planning... 2026-01-24 16:45:13,803 - agents.planner_agent - INFO - 🧠 Planner analyzing: Show employees from the backend team 2026-01-24 16:45:13,803 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:45:51,250 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:45:51,250 - agents.planner_agent - INFO - LLM Response: Assuming that the user's query is "Show employees from the backend team," the following MCP (Mysql Compatible Query) tool will be chosen: Tool Name: MYSQL_QUERY_PLANNER Parameters: 1. Department - I... 2026-01-24 16:45:51,251 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'MYSQL_QUERY_PLANNER', 'parameters': {'department': 'value', 'status': 'value', 'priority': 'value'}, 'reasoning': "Why you chose this tool: This tool uses the available_tools list to choose a suitable MCP query based on the user's query parameters. The response is in JSON format and contains the chosen tool, its parameters, and reason for using it."} 2026-01-24 16:45:51,252 - agents.orchestrator - INFO - [2/3] ⚙️ Executing... 2026-01-24 16:45:51,252 - agents.executor_agent - INFO - ⚙️ Executing plan: {'tool': 'MYSQL_QUERY_PLANNER', 'parameters': {'department': 'value', 'status': 'value', 'priority': 'value'}, 'reasoning': "Why you chose this tool: This tool uses the available_tools list to choose a suitable MCP query based on the user's query parameters. The response is in JSON format and contains the chosen tool, its parameters, and reason for using it."} 2026-01-24 16:45:51,252 - agents.executor_agent - INFO - Tool: MYSQL_QUERY_PLANNER, Parameters: {'department': 'value', 'status': 'value', 'priority': 'value'} 2026-01-24 16:45:51,252 - agents.executor_agent - ERROR - ❌ Tool not found: MYSQL_QUERY_PLANNER 2026-01-24 16:45:51,252 - agents.orchestrator - ERROR - Execution error: Tool 'MYSQL_QUERY_PLANNER' not allowed 2026-01-24 16:47:50,528 - agents.orchestrator - INFO - Initializing Orchestrator with ollama (tinyllama) 2026-01-24 16:47:50,528 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:47:50,528 - agents.orchestrator - INFO - Processing Query: Show me all employees in the AI department 2026-01-24 16:47:50,528 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:47:50,528 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 16:47:50,528 - agents.planner_agent - INFO - Planner analyzing: Show me all employees in the AI department 2026-01-24 16:47:50,528 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:48:01,676 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:48:01,676 - agents.planner_agent - INFO - LLM Response: Based on the given user query and available tools, the "Fetcch" MCP tool would be called to execute a request to show all employees in the AI department. Here's how it could look like in an API respon... 2026-01-24 16:48:01,677 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'Fetcch tool chosen because AI department is specified in user query.'} 2026-01-24 16:48:01,677 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 16:48:01,677 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'Fetcch tool chosen because AI department is specified in user query.'} 2026-01-24 16:48:01,677 - agents.executor_agent - INFO - Tool: mcp_get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 16:48:01,677 - agents.executor_agent - INFO - Calling tool: mcp_get_employees_by_department 2026-01-24 16:48:01,677 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 16:48:01,745 - database.db_executor - INFO - Connected to database successfully 2026-01-24 16:48:01,748 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 16:48:01,748 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 16:48:01,748 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 16:48:01,748 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 16:48:01,748 - agents.orchestrator - ERROR - ❌ Orchestrator error: Object of type Decimal is not JSON serializable 2026-01-24 16:48:01,748 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:48:01,748 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 16:48:01,748 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:48:01,748 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 16:48:01,748 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 16:48:01,753 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:48:14,825 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:48:14,826 - agents.planner_agent - INFO - LLM Response: As a Database Query Planning Assistant, here's an example response to the user query "What are the projects that are in progress?" using the available tools: Respond ONLY with valid JSON in this form... 2026-01-24 16:48:14,826 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_projects_by_status', 'parameters': {'status': 'Completed'}, 'reasoning': 'The projects with a status of Completed are: 1. Project1 - Department AI, Status: Completed'} 2026-01-24 16:48:14,827 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 16:48:14,827 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_projects_by_status', 'parameters': {'status': 'Completed'}, 'reasoning': 'The projects with a status of Completed are: 1. Project1 - Department AI, Status: Completed'} 2026-01-24 16:48:14,827 - agents.executor_agent - INFO - Tool: mcp_get_projects_by_status, Parameters: {'status': 'Completed'} 2026-01-24 16:48:14,827 - agents.executor_agent - INFO - Calling tool: mcp_get_projects_by_status 2026-01-24 16:48:14,827 - mcp.tools - INFO - Tool called: get_projects_by_status with input=Completed 2026-01-24 16:48:14,906 - database.db_executor - INFO - Connected to database successfully 2026-01-24 16:48:14,913 - database.db_executor - INFO - Found 1 projects with status Completed 2026-01-24 16:48:14,913 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 1 rows 2026-01-24 16:48:14,913 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 16:48:14,913 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 16:48:14,913 - agents.orchestrator - ERROR - ❌ Orchestrator error: Object of type date is not JSON serializable 2026-01-24 16:48:14,913 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:48:14,913 - agents.orchestrator - INFO - Processing Query: Get me all high priority issues 2026-01-24 16:48:14,913 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:48:14,913 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 16:48:14,913 - agents.planner_agent - INFO - Planner analyzing: Get me all high priority issues 2026-01-24 16:48:14,913 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:48:42,217 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:48:42,218 - agents.planner_agent - INFO - LLM Response: Based on the available tools and user query, "Get me all high priority issues", a MCP tool to be called would be: 1. **tool_name**: A tool that supports a specific method of query planning (e.g., SQL... 2026-01-24 16:48:42,218 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'tool_name', 'parameters': {'department': 'value'}, 'reasoning': "To get high priority issues with a specific status and priority level, please enter the department number or name in the 'depatment' parameter."} 2026-01-24 16:48:42,220 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 16:48:42,220 - agents.executor_agent - INFO - Executing plan: {'tool': 'tool_name', 'parameters': {'department': 'value'}, 'reasoning': "To get high priority issues with a specific status and priority level, please enter the department number or name in the 'depatment' parameter."} 2026-01-24 16:48:42,221 - agents.executor_agent - INFO - Tool: tool_name, Parameters: {'department': 'value'} 2026-01-24 16:48:42,221 - agents.executor_agent - ERROR - ❌ Tool not found: tool_name 2026-01-24 16:48:42,221 - agents.orchestrator - ERROR - Execution error: Tool 'tool_name' not allowed 2026-01-24 16:48:42,223 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:48:42,224 - agents.orchestrator - INFO - Processing Query: Show employees from the backend team 2026-01-24 16:48:42,224 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:48:42,224 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 16:48:42,224 - agents.planner_agent - INFO - Planner analyzing: Show employees from the backend team 2026-01-24 16:48:42,224 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:49:19,265 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:49:19,268 - agents.planner_agent - INFO - LLM Response: To choose an appropriate MCP tool based on the available tools, we should first extract the parameter value from the user query. For this example user query, "Show employees from the backend team", we... 2026-01-24 16:49:19,269 - agents.planner_agent - ERROR - JSON parsing error: Expecting property name enclosed in double quotes: line 1 column 3 (char 2) 2026-01-24 16:49:19,269 - agents.orchestrator - ERROR - Planning error: JSON parsing error: Expecting property name enclosed in double quotes: line 1 column 3 (char 2) 2026-01-24 16:57:40,462 - test - INFO - This should not appear in console 2026-01-24 16:58:32,119 - agents.orchestrator - INFO - Initializing Orchestrator with ollama (tinyllama) 2026-01-24 16:58:32,127 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:58:32,128 - agents.orchestrator - INFO - Processing Query: Show me all employees in the AI department 2026-01-24 16:58:32,128 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:58:32,128 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 16:58:32,128 - agents.planner_agent - INFO - Planner analyzing: Show me all employees in the AI department 2026-01-24 16:58:32,128 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:59:11,657 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:59:11,658 - agents.planner_agent - INFO - LLM Response: Based on the user query and available tools, I would choose to call the `mcp_get_employees_by_department` MCP tool. Here's a sample response: ```json { "tool": "mcp_get_employees_by_department", ... 2026-01-24 16:59:11,658 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': "This tool can be used to fetch employees from the AI department, so we'll extract the 'value' parameter and use it to get the expected result."} 2026-01-24 16:59:11,658 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 16:59:11,658 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': "This tool can be used to fetch employees from the AI department, so we'll extract the 'value' parameter and use it to get the expected result."} 2026-01-24 16:59:11,658 - agents.executor_agent - INFO - Tool: mcp_get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 16:59:11,658 - agents.executor_agent - INFO - Calling tool: mcp_get_employees_by_department 2026-01-24 16:59:11,658 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 16:59:11,847 - database.db_executor - INFO - Connected to database successfully 2026-01-24 16:59:11,906 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 16:59:11,907 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 16:59:11,907 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 16:59:11,908 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 16:59:11,908 - agents.orchestrator - ERROR - ❌ Orchestrator error: Object of type Decimal is not JSON serializable 2026-01-24 16:59:11,909 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:59:11,909 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 16:59:11,909 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:59:11,909 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 16:59:11,909 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 16:59:11,909 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:59:37,510 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:59:37,510 - agents.planner_agent - INFO - LLM Response: According to the given user query, "What are the projects that are in progress?" as the project department is not mentioned, we can only use the available MCP tools to retrieve data. The available M... 2026-01-24 16:59:37,511 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_projects_by_status', 'parameters': {'status': 'Completed'}, 'reasoning': 'Retrieve FeTCH projects with a specific status (complete)'} 2026-01-24 16:59:37,511 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 16:59:37,512 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_projects_by_status', 'parameters': {'status': 'Completed'}, 'reasoning': 'Retrieve FeTCH projects with a specific status (complete)'} 2026-01-24 16:59:37,512 - agents.executor_agent - INFO - Tool: mcp_get_projects_by_status, Parameters: {'status': 'Completed'} 2026-01-24 16:59:37,512 - agents.executor_agent - INFO - Calling tool: mcp_get_projects_by_status 2026-01-24 16:59:37,512 - mcp.tools - INFO - Tool called: get_projects_by_status with input=Completed 2026-01-24 16:59:37,565 - database.db_executor - INFO - Connected to database successfully 2026-01-24 16:59:37,568 - database.db_executor - INFO - Found 1 projects with status Completed 2026-01-24 16:59:37,568 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 1 rows 2026-01-24 16:59:37,568 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 16:59:37,568 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 16:59:37,568 - agents.orchestrator - ERROR - ❌ Orchestrator error: Object of type date is not JSON serializable 2026-01-24 16:59:37,568 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:59:37,568 - agents.orchestrator - INFO - Processing Query: Get me all high priority issues 2026-01-24 16:59:37,568 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:59:37,568 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 16:59:37,568 - agents.planner_agent - INFO - Planner analyzing: Get me all high priority issues 2026-01-24 16:59:37,568 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 16:59:47,628 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 16:59:47,628 - agents.planner_agent - INFO - LLM Response: To use the MCPIssuesGetByDepartment tool, you can pass the "department" parameter to it like this: User Query: "Get me all high priority issues by department" Respond: {"tool": "mcpips", "parameters... 2026-01-24 16:59:47,628 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcpips', 'parameters': {'depatiment': 'value'}, 'reasoning': 'High priority issues for department with value'} 2026-01-24 16:59:47,628 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 16:59:47,628 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcpips', 'parameters': {'depatiment': 'value'}, 'reasoning': 'High priority issues for department with value'} 2026-01-24 16:59:47,628 - agents.executor_agent - INFO - Tool: mcpips, Parameters: {'depatiment': 'value'} 2026-01-24 16:59:47,628 - agents.executor_agent - ERROR - ❌ Tool not found: mcpips 2026-01-24 16:59:47,628 - agents.orchestrator - ERROR - Execution error: Tool 'mcpips' not allowed 2026-01-24 16:59:47,630 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:59:47,630 - agents.orchestrator - INFO - Processing Query: Show employees from the backend team 2026-01-24 16:59:47,630 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 16:59:47,630 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 16:59:47,630 - agents.planner_agent - INFO - Planner analyzing: Show employees from the backend team 2026-01-24 16:59:47,630 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:00:29,353 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 17:00:29,353 - agents.planner_agent - INFO - LLM Response: To choose which MCPI (Management Query Planner Interface) tool to call based on a given user query, we need to extract the parameter value from the user query and compare it against the available tool... 2026-01-24 17:00:29,353 - agents.planner_agent - ERROR - JSON parsing error: Extra data: line 21 column 1 (char 575) 2026-01-24 17:00:29,353 - agents.orchestrator - ERROR - Planning error: JSON parsing error: Extra data: line 21 column 1 (char 575) 2026-01-24 17:02:26,156 - agents.orchestrator - INFO - Initializing Orchestrator with ollama (tinyllama) 2026-01-24 17:02:26,158 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 17:02:26,158 - agents.orchestrator - INFO - Processing Query: Show me all employees in the AI department 2026-01-24 17:02:26,158 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 17:02:26,158 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 17:02:26,158 - agents.planner_agent - INFO - Planner analyzing: Show me all employees in the AI department 2026-01-24 17:02:26,159 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:03:28,240 - agents.llm_provider - ERROR - ❌ Ollama error: HTTPConnectionPool(host='localhost', port=11434): Read timed out. (read timeout=60) 2026-01-24 17:03:28,241 - agents.planner_agent - ERROR - Planning failed: HTTPConnectionPool(host='localhost', port=11434): Read timed out. (read timeout=60) 2026-01-24 17:03:28,241 - agents.orchestrator - ERROR - Planning error: HTTPConnectionPool(host='localhost', port=11434): Read timed out. (read timeout=60) 2026-01-24 17:03:28,242 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 17:03:28,243 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 17:03:28,243 - agents.orchestrator - INFO - ====================================================================== 2026-01-24 17:03:28,243 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 17:03:28,243 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 17:03:28,243 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:05:11,382 - agents.orchestrator - INFO - Initializing Orchestrator with ollama (tinyllama) 2026-01-24 17:05:11,383 - agents.orchestrator - INFO - Processing Query: Show me all employees in the AI department? 2026-01-24 17:05:11,383 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 17:05:11,383 - agents.planner_agent - INFO - Planner analyzing: Show me all employees in the AI department? 2026-01-24 17:05:11,383 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:05:39,730 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 17:05:39,730 - agents.planner_agent - INFO - LLM Response: Certainly! Here's a completed JSON response for the "Show me all employees in the AI department?" query: ```json { "tool": "mcpcr", "parameters": { "department": "AI" }, "reasoning": "McP... 2026-01-24 17:05:39,732 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'McP Crawler'} 2026-01-24 17:05:39,732 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 17:05:39,732 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'McP Crawler'} 2026-01-24 17:05:39,732 - agents.executor_agent - INFO - Tool: mcp_get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 17:05:39,732 - agents.executor_agent - INFO - Calling tool: mcp_get_employees_by_department 2026-01-24 17:05:39,732 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 17:05:40,047 - database.db_executor - INFO - Connected to database successfully 2026-01-24 17:05:40,065 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 17:05:40,065 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 17:05:40,065 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 17:05:40,065 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 17:05:40,067 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:06:35,148 - agents.orchestrator - INFO - Initializing Orchestrator with ollama (tinyllama) 2026-01-24 17:06:35,148 - agents.orchestrator - INFO - Processing Query: Show me all employees in the AI department? 2026-01-24 17:06:35,148 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 17:06:35,148 - agents.planner_agent - INFO - Planner analyzing: Show me all employees in the AI department? 2026-01-24 17:06:35,148 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:06:57,079 - agents.orchestrator - INFO - Initializing Orchestrator with ollama (tinyllama) 2026-01-24 17:06:57,080 - agents.orchestrator - INFO - Processing Query: Show me all employees in the AI department? 2026-01-24 17:06:57,080 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 17:06:57,080 - agents.planner_agent - INFO - Planner analyzing: Show me all employees in the AI department? 2026-01-24 17:06:57,080 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:07:04,172 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 17:07:04,172 - agents.planner_agent - INFO - LLM Response: Response: This JSON format is for a query planter, which can be used to search through various tools and find the one that best matches your specific query. To use this format, you would simply paste ... 2026-01-24 17:07:04,172 - agents.planner_agent - ERROR - No JSON found in response 2026-01-24 17:07:04,173 - agents.orchestrator - ERROR - Planning error: Could not parse LLM response - no JSON found 2026-01-24 17:07:04,173 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 17:07:04,173 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 17:07:04,174 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 17:07:04,174 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:07:13,382 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 17:07:13,383 - agents.planner_agent - INFO - LLM Response: Sure, here's a possible JSON response for the requested task: { "tool": "mcapi", "parameters": { "department": "AI" }, "reasoning": "Based on the department parameter, FeTCH's mcapi... 2026-01-24 17:07:13,383 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_projects_by_status', 'parameters': {'department': 'AI'}, 'reasoning': "Based on the department parameter, FeTCH's mcapi is the best tool that matches this query."} 2026-01-24 17:07:13,383 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 17:07:13,383 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_projects_by_status', 'parameters': {'department': 'AI'}, 'reasoning': "Based on the department parameter, FeTCH's mcapi is the best tool that matches this query."} 2026-01-24 17:07:13,383 - agents.executor_agent - INFO - Tool: mcp_get_projects_by_status, Parameters: {'department': 'AI'} 2026-01-24 17:07:13,383 - agents.executor_agent - INFO - Calling tool: mcp_get_projects_by_status 2026-01-24 17:07:13,383 - mcp.tools - INFO - Tool called: get_projects_by_status with input=AI 2026-01-24 17:07:13,460 - database.db_executor - INFO - Connected to database successfully 2026-01-24 17:07:13,463 - database.db_executor - INFO - Found 0 projects with status AI 2026-01-24 17:07:13,463 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 0 rows 2026-01-24 17:07:13,463 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 17:07:13,463 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 17:07:13,463 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:07:26,758 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 17:07:26,758 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 17:07:26,759 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 17:07:26,760 - agents.orchestrator - INFO - Processing Query: Get me all high priority issues? 2026-01-24 17:07:26,760 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 17:07:26,760 - agents.planner_agent - INFO - Planner analyzing: Get me all high priority issues? 2026-01-24 17:07:26,760 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:07:48,976 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 17:07:48,976 - agents.planner_agent - INFO - LLM Response: Sure! Here's an updated version of the query and response format using the `mcpexpand` tool: Query: "Get me all high priority issues?" Task: Using the `mcpexpand` tool, expand the parameter value `"... 2026-01-24 17:07:48,976 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_issues_by_priority', 'parameters': [['name', 'get_issues']], 'reasoning': None} 2026-01-24 17:07:48,976 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 17:07:48,976 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_issues_by_priority', 'parameters': [['name', 'get_issues']], 'reasoning': None} 2026-01-24 17:07:48,976 - agents.executor_agent - INFO - Tool: mcp_get_issues_by_priority, Parameters: [['name', 'get_issues']] 2026-01-24 17:07:48,976 - agents.executor_agent - INFO - Calling tool: mcp_get_issues_by_priority 2026-01-24 17:07:48,976 - agents.executor_agent - ERROR - ❌ Error executing tool: 'list' object has no attribute 'values' 2026-01-24 17:07:48,976 - agents.orchestrator - ERROR - Execution error: Error: 'list' object has no attribute 'values' 2026-01-24 17:09:36,071 - agents.orchestrator - INFO - Initializing Orchestrator with ollama (tinyllama) 2026-01-24 17:09:36,083 - agents.orchestrator - INFO - Processing Query: Show me all employees in the AI department? 2026-01-24 17:09:36,083 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 17:09:36,083 - agents.planner_agent - INFO - Planner analyzing: Show me all employees in the AI department? 2026-01-24 17:09:36,084 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:10:39,861 - agents.orchestrator - INFO - Initializing Orchestrator with ollama (tinyllama) 2026-01-24 17:10:39,862 - agents.orchestrator - INFO - Processing Query: Show me all employees in the AI department? 2026-01-24 17:10:39,862 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 17:10:39,862 - agents.planner_agent - INFO - Planner analyzing: Show me all employees in the AI department? 2026-01-24 17:10:39,863 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:10:56,213 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 17:10:56,214 - agents.planner_agent - INFO - LLM Response: Here is a possible response to the query "Show me all employees in the AI department?": Query plan: 1. Search for "mcp_get_employees_by_department" in the database and retrieve data as described in t... 2026-01-24 17:10:56,215 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'reason'} 2026-01-24 17:10:56,216 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 17:10:56,216 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'reason'} 2026-01-24 17:10:56,217 - agents.executor_agent - INFO - Tool: mcp_get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 17:10:56,217 - agents.executor_agent - INFO - Calling tool: mcp_get_employees_by_department 2026-01-24 17:10:56,218 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 17:10:56,362 - database.db_executor - INFO - Connected to database successfully 2026-01-24 17:10:56,381 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 17:10:56,381 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 17:10:56,381 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 17:10:56,381 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 17:10:56,381 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:11:30,531 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 17:11:30,532 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 17:11:30,532 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 17:11:30,532 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 17:11:30,532 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 17:11:30,532 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 17:11:30,532 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:12:02,348 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 17:12:02,348 - agents.planner_agent - INFO - LLM Response: To respond to the query "What are the projects that are in progress?", you would need to use the `mcp_get_projects_by_status` tool, which is available under the `"Exact Tool Name"` category. Here's h... 2026-01-24 17:12:02,348 - agents.planner_agent - WARNING - No JSON found in response. Attempting fallback strategy. 2026-01-24 17:12:02,349 - agents.planner_agent - INFO - ✅ Fallback Plan: {'tool': 'mcp_get_projects_by_status', 'parameters': {'status': 'In Progress'}, 'reasoning': 'Fallback strategy used due to LLM parsing failure'} 2026-01-24 17:12:02,349 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 17:12:02,349 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_projects_by_status', 'parameters': {'status': 'In Progress'}, 'reasoning': 'Fallback strategy used due to LLM parsing failure'} 2026-01-24 17:12:02,349 - agents.executor_agent - INFO - Tool: mcp_get_projects_by_status, Parameters: {'status': 'In Progress'} 2026-01-24 17:12:02,349 - agents.executor_agent - INFO - Calling tool: mcp_get_projects_by_status 2026-01-24 17:12:02,349 - mcp.tools - INFO - Tool called: get_projects_by_status with input=In Progress 2026-01-24 17:12:02,415 - database.db_executor - INFO - Connected to database successfully 2026-01-24 17:12:02,427 - database.db_executor - INFO - Found 2 projects with status In Progress 2026-01-24 17:12:02,428 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 2 rows 2026-01-24 17:12:02,428 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 17:12:02,428 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 17:12:02,428 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:12:26,708 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 17:12:26,709 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 17:12:26,710 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 17:12:26,710 - agents.orchestrator - INFO - Processing Query: Get me all high priority issues? 2026-01-24 17:12:26,710 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 17:12:26,710 - agents.planner_agent - INFO - Planner analyzing: Get me all high priority issues? 2026-01-24 17:12:26,711 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:12:53,241 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 17:12:53,242 - agents.planner_agent - INFO - LLM Response: Response to the query "Get me all high priority issues?" is given below: Task: The "mcp_get_issues_by_priority" tool matches the query by extracting the parameter with name "priority". Extracting thi... 2026-01-24 17:12:53,242 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_issues_by_priority', 'parameters': {'param': 'High'}, 'reasoning': 'reason'} 2026-01-24 17:12:53,242 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 17:12:53,242 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_issues_by_priority', 'parameters': {'param': 'High'}, 'reasoning': 'reason'} 2026-01-24 17:12:53,242 - agents.executor_agent - INFO - Tool: mcp_get_issues_by_priority, Parameters: {'param': 'High'} 2026-01-24 17:12:53,242 - agents.executor_agent - INFO - Calling tool: mcp_get_issues_by_priority 2026-01-24 17:12:53,242 - mcp.tools - INFO - Tool called: get_issues_by_priority with input=High 2026-01-24 17:12:53,366 - database.db_executor - INFO - Connected to database successfully 2026-01-24 17:12:53,376 - database.db_executor - INFO - Found 2 issues with priority High 2026-01-24 17:12:53,377 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 2 rows 2026-01-24 17:12:53,377 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 17:12:53,377 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 17:12:53,377 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:13:26,364 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 17:13:26,367 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 17:13:26,367 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 17:14:06,986 - agents.orchestrator - INFO - Initializing Orchestrator with ollama (tinyllama) 2026-01-24 17:14:06,986 - agents.orchestrator - INFO - Processing Query: Fetch employee details where department is AI. 2026-01-24 17:14:06,988 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 17:14:06,988 - agents.planner_agent - INFO - Planner analyzing: Fetch employee details where department is AI. 2026-01-24 17:14:06,988 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:14:37,361 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 17:14:37,361 - agents.planner_agent - INFO - LLM Response: Response: The AI-based tool with the highest matching parameter is Exact Tool Name. In this case, the reason why it matches is due to the parameter "department" being equal to "AI". Explanation of t... 2026-01-24 17:14:37,362 - agents.planner_agent - WARNING - No JSON found in response. Attempting fallback strategy. 2026-01-24 17:14:37,362 - agents.planner_agent - INFO - ✅ Fallback Plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'Fallback strategy used due to LLM parsing failure'} 2026-01-24 17:14:37,362 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 17:14:37,362 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'Fallback strategy used due to LLM parsing failure'} 2026-01-24 17:14:37,362 - agents.executor_agent - INFO - Tool: mcp_get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 17:14:37,362 - agents.executor_agent - INFO - Calling tool: mcp_get_employees_by_department 2026-01-24 17:14:37,362 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 17:14:37,426 - database.db_executor - INFO - Connected to database successfully 2026-01-24 17:14:37,431 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 17:14:37,431 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 17:14:37,431 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 17:14:37,431 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 17:14:37,431 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:15:00,651 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 17:15:00,651 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 17:15:00,651 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 17:15:00,653 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 17:15:00,653 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 17:15:00,653 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 17:15:00,654 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:15:28,517 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 17:15:28,518 - agents.planner_agent - INFO - LLM Response: Certainly! Here's a complete query planner that meets the requirements you mentioned: 1. Query Plan: - Parameter: "department" (type: STRING) - Tool: "mcp_get_employees_by_department" - Reasoning: F... 2026-01-24 17:15:28,518 - agents.planner_agent - WARNING - No JSON found in response. Attempting fallback strategy. 2026-01-24 17:15:28,518 - agents.planner_agent - INFO - ✅ Fallback Plan: {'tool': 'mcp_get_projects_by_status', 'parameters': {'status': 'In Progress'}, 'reasoning': 'Fallback strategy used due to LLM parsing failure'} 2026-01-24 17:15:28,519 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 17:15:28,519 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_projects_by_status', 'parameters': {'status': 'In Progress'}, 'reasoning': 'Fallback strategy used due to LLM parsing failure'} 2026-01-24 17:15:28,519 - agents.executor_agent - INFO - Tool: mcp_get_projects_by_status, Parameters: {'status': 'In Progress'} 2026-01-24 17:15:28,519 - agents.executor_agent - INFO - Calling tool: mcp_get_projects_by_status 2026-01-24 17:15:28,519 - mcp.tools - INFO - Tool called: get_projects_by_status with input=In Progress 2026-01-24 17:15:28,641 - database.db_executor - INFO - Connected to database successfully 2026-01-24 17:15:28,646 - database.db_executor - INFO - Found 2 projects with status In Progress 2026-01-24 17:15:28,647 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 2 rows 2026-01-24 17:15:28,647 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 17:15:28,647 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 17:15:28,648 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 17:16:08,586 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 17:16:08,586 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 17:16:08,586 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 19:18:10,793 - test_logger - INFO - If you see this, the logger initialized correctly without crashing! 2026-01-24 19:27:28,044 - agents.orchestrator - INFO - Initializing Orchestrator with ollama (tinyllama) 2026-01-24 19:27:28,045 - agents.orchestrator - INFO - Processing Query: Fetch employee details where department is AI. 2026-01-24 19:27:28,045 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 19:27:28,045 - agents.planner_agent - INFO - Planner analyzing: Fetch employee details where department is AI. 2026-01-24 19:27:28,045 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 19:27:58,029 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 19:27:58,029 - agents.planner_agent - INFO - LLM Response: The "mcp_get_employees_by_department" query planner is a query optimizer that uses a technique called _Fastest Inserts, which is a method of selecting the most efficient data insertion strategy for ea... 2026-01-24 19:27:58,030 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'status': 'Completed'}, 'reasoning': 'The query optimizer has selected the Fastest Inserts strategy for the department and status parameter combination.'} 2026-01-24 19:27:58,030 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 19:27:58,030 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'status': 'Completed'}, 'reasoning': 'The query optimizer has selected the Fastest Inserts strategy for the department and status parameter combination.'} 2026-01-24 19:27:58,030 - agents.executor_agent - INFO - Tool: mcp_get_employees_by_department, Parameters: {'status': 'Completed'} 2026-01-24 19:27:58,030 - agents.executor_agent - INFO - Calling tool: mcp_get_employees_by_department 2026-01-24 19:27:58,030 - mcp.tools - INFO - Tool called: get_employees_by_department with input=Completed 2026-01-24 19:27:58,264 - database.db_executor - INFO - Connected to database successfully 2026-01-24 19:27:58,307 - database.db_executor - INFO - Found 0 employees in Completed department 2026-01-24 19:27:58,310 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 0 rows 2026-01-24 19:27:58,310 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 19:27:58,310 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 19:27:58,310 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 19:28:20,791 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 19:28:20,792 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 19:28:20,792 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 19:28:20,793 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 19:28:20,793 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 19:28:20,793 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 19:28:20,793 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 19:28:50,166 - agents.llm_provider - INFO - ✅ Ollama response received 2026-01-24 19:28:50,166 - agents.planner_agent - INFO - LLM Response: The available tools for query planning in your system are: 1. "mcp_get_employees_by_department": this tool gets employees from a specific department (e.g., AI, Backend, Frontend, DevOps, Data Science)... 2026-01-24 19:28:50,166 - agents.planner_agent - WARNING - No JSON found in response. Attempting fallback strategy. 2026-01-24 19:28:50,166 - agents.planner_agent - INFO - ✅ Fallback Plan: {'tool': 'mcp_get_projects_by_status', 'parameters': {'status': 'In Progress'}, 'reasoning': 'Fallback strategy used due to LLM parsing failure'} 2026-01-24 19:28:50,166 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 19:28:50,166 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_projects_by_status', 'parameters': {'status': 'In Progress'}, 'reasoning': 'Fallback strategy used due to LLM parsing failure'} 2026-01-24 19:28:50,166 - agents.executor_agent - INFO - Tool: mcp_get_projects_by_status, Parameters: {'status': 'In Progress'} 2026-01-24 19:28:50,166 - agents.executor_agent - INFO - Calling tool: mcp_get_projects_by_status 2026-01-24 19:28:50,166 - mcp.tools - INFO - Tool called: get_projects_by_status with input=In Progress 2026-01-24 19:28:50,256 - database.db_executor - INFO - Connected to database successfully 2026-01-24 19:28:50,262 - database.db_executor - INFO - Found 2 projects with status In Progress 2026-01-24 19:28:50,262 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 2 rows 2026-01-24 19:28:50,262 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 19:28:50,262 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 19:28:50,262 - agents.llm_provider - INFO - Calling Ollama (tinyllama)... 2026-01-24 19:54:29,690 - __main__ - INFO - ✅ Database initialized successfully with sample data. 2026-01-24 19:54:29,690 - __main__ - INFO - Verified: 5 employees inserted. 2026-01-24 19:54:54,809 - agents.orchestrator - INFO - Initializing Orchestrator with mock (tinyllama) 2026-01-24 19:54:54,810 - agents.orchestrator - INFO - Processing Query: Fetch employee details where department is AI. 2026-01-24 19:54:54,810 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 19:54:54,810 - agents.planner_agent - INFO - Planner analyzing: Fetch employee details where department is AI. 2026-01-24 19:54:54,810 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 19:54:54,810 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": "AI"}... 2026-01-24 19:54:54,810 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_employees_by_department', 'parameters': 'AI'} 2026-01-24 19:54:54,810 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 19:54:54,810 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_employees_by_department', 'parameters': 'AI'} 2026-01-24 19:54:54,810 - agents.executor_agent - INFO - Tool: mcp_get_employees_by_department, Parameters: AI 2026-01-24 19:54:54,810 - agents.executor_agent - INFO - Calling tool: mcp_get_employees_by_department 2026-01-24 19:54:54,811 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 19:54:54,814 - database.db_executor - INFO - Found 2 employees in AI department 2026-01-24 19:54:54,814 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 2 rows 2026-01-24 19:54:54,814 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 19:54:54,814 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 19:54:54,814 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 19:54:54,814 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 19:54:54,814 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 19:54:54,815 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 19:54:54,815 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 19:54:54,815 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 19:54:54,815 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 19:54:54,816 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": "AI"}... 2026-01-24 19:54:54,816 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_projects_by_status', 'parameters': 'AI'} 2026-01-24 19:54:54,816 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 19:54:54,816 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_projects_by_status', 'parameters': 'AI'} 2026-01-24 19:54:54,816 - agents.executor_agent - INFO - Tool: mcp_get_projects_by_status, Parameters: AI 2026-01-24 19:54:54,816 - agents.executor_agent - INFO - Calling tool: mcp_get_projects_by_status 2026-01-24 19:54:54,816 - mcp.tools - INFO - Tool called: get_projects_by_status with input=AI 2026-01-24 19:54:54,816 - database.db_executor - INFO - Found 0 projects with status AI 2026-01-24 19:54:54,818 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 0 rows 2026-01-24 19:54:54,818 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 19:54:54,819 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 19:54:54,819 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 19:54:54,819 - agents.llm_provider - WARNING - Mock provider got unknown prompt: User asked: "What are the projects that are in progress?" Database returned: [] Provide a clear, concise explanation of: 1. What data was retrieved 2. Key insights or patterns 3. Any recommendations Be conversational and helpful. Keep response to 3-4 sentences. 2026-01-24 19:54:54,819 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 19:54:54,820 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 19:56:29,954 - agents.orchestrator - INFO - Initializing Orchestrator with mock (tinyllama) 2026-01-24 19:56:29,954 - agents.orchestrator - INFO - Processing Query: Fetch employee details where department is AI. 2026-01-24 19:56:29,955 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 19:56:29,955 - agents.planner_agent - INFO - Planner analyzing: Fetch employee details where department is AI. 2026-01-24 19:56:29,955 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 19:56:29,955 - agents.planner_agent - INFO - LLM Response: {"tool": "mcp_get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 19:56:29,955 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 19:56:29,955 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 19:56:29,955 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 19:56:29,955 - agents.executor_agent - INFO - Tool: mcp_get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 19:56:29,955 - agents.executor_agent - INFO - Calling tool: mcp_get_employees_by_department 2026-01-24 19:56:29,955 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 19:56:29,965 - database.db_executor - INFO - Found 2 employees in AI department 2026-01-24 19:56:29,965 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 2 rows 2026-01-24 19:56:29,965 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 19:56:29,966 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 19:56:29,966 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 19:56:29,966 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 19:56:29,966 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 19:56:29,966 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 19:56:29,966 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 19:56:29,967 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 19:56:29,967 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 19:56:29,967 - agents.planner_agent - INFO - LLM Response: {"tool": "mcp_get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 19:56:29,967 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 19:56:29,967 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 19:56:29,967 - agents.executor_agent - INFO - Executing plan: {'tool': 'mcp_get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 19:56:29,967 - agents.executor_agent - INFO - Tool: mcp_get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 19:56:29,967 - agents.executor_agent - INFO - Calling tool: mcp_get_employees_by_department 2026-01-24 19:56:29,967 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 19:56:29,970 - database.db_executor - INFO - Found 2 employees in AI department 2026-01-24 19:56:29,970 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 2 rows 2026-01-24 19:56:29,971 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 19:56:29,971 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 19:56:29,972 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 19:56:29,972 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 19:56:29,972 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 20:03:17,566 - agents.orchestrator - INFO - Initializing Orchestrator with mock (tinyllama) 2026-01-24 20:03:17,567 - agents.orchestrator - INFO - Processing Query: Fetch employee details where department is AI. 2026-01-24 20:03:17,567 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 20:03:17,568 - agents.planner_agent - INFO - Planner analyzing: Fetch employee details where department is AI. 2026-01-24 20:03:17,568 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 20:03:17,568 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 20:03:17,569 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 20:03:17,569 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 20:03:17,569 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 20:03:17,569 - agents.executor_agent - INFO - Tool: get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 20:03:17,569 - agents.executor_agent - INFO - Calling tool: get_employees_by_department 2026-01-24 20:03:17,569 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 20:03:17,742 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 20:03:17,743 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 20:03:17,743 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 20:03:17,743 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 20:03:17,744 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 20:03:17,744 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 20:03:17,744 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 20:03:17,745 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 20:03:17,746 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 20:03:17,746 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 20:03:17,746 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 20:03:17,746 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 20:03:17,746 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 20:03:17,746 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 20:03:17,746 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 20:03:17,747 - agents.executor_agent - INFO - Tool: get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 20:03:17,747 - agents.executor_agent - INFO - Calling tool: get_employees_by_department 2026-01-24 20:03:17,747 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 20:03:17,891 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 20:03:17,891 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 20:03:17,891 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 20:03:17,891 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 20:03:17,891 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 20:03:17,891 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 20:03:17,891 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 20:04:50,621 - agents.orchestrator - INFO - Initializing Orchestrator with mock (tinyllama) 2026-01-24 20:04:50,621 - agents.orchestrator - INFO - Processing Query: Fetch employee details where department is AI. 2026-01-24 20:04:50,621 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 20:04:50,621 - agents.planner_agent - INFO - Planner analyzing: Fetch employee details where department is AI. 2026-01-24 20:04:50,621 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 20:04:50,621 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 20:04:50,621 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 20:04:50,621 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 20:04:50,621 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 20:04:50,621 - agents.executor_agent - INFO - Tool: get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 20:04:50,621 - agents.executor_agent - INFO - Calling tool: get_employees_by_department 2026-01-24 20:04:50,621 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 20:04:50,681 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 20:04:50,681 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 20:04:50,681 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 20:04:50,681 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 20:04:50,682 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 20:04:50,682 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 20:04:50,682 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 20:04:50,682 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 20:04:50,682 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 20:04:50,682 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 20:04:50,682 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 20:04:50,682 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 20:04:50,682 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 20:04:50,682 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 20:04:50,682 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 20:04:50,683 - agents.executor_agent - INFO - Tool: get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 20:04:50,683 - agents.executor_agent - INFO - Calling tool: get_employees_by_department 2026-01-24 20:04:50,683 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 20:04:50,738 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 20:04:50,739 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 20:04:50,739 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 20:04:50,739 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 20:04:50,739 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 20:04:50,739 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 20:04:50,739 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 20:17:16,422 - agents.orchestrator - INFO - Initializing Orchestrator with mock (tinyllama) 2026-01-24 20:17:16,424 - agents.orchestrator - INFO - Processing Query: Fetch employee details where department is AI. 2026-01-24 20:17:16,424 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 20:17:16,424 - agents.planner_agent - INFO - Planner analyzing: Fetch employee details where department is AI. 2026-01-24 20:17:16,424 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 20:17:16,424 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 20:17:16,426 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 20:17:16,426 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 20:17:16,426 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 20:17:16,426 - agents.executor_agent - INFO - Tool: get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 20:17:16,426 - agents.executor_agent - INFO - Calling tool: get_employees_by_department 2026-01-24 20:17:16,426 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 20:17:16,569 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 20:17:16,569 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 20:17:16,569 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 20:17:16,569 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 20:17:16,569 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 20:17:16,569 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 20:17:16,569 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 20:17:16,569 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 20:17:16,569 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 20:17:16,569 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 20:17:16,569 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 20:17:16,569 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 20:17:16,569 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 20:17:16,569 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 20:17:16,569 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 20:17:16,569 - agents.executor_agent - INFO - Tool: get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 20:17:16,569 - agents.executor_agent - INFO - Calling tool: get_employees_by_department 2026-01-24 20:17:16,569 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 20:17:16,675 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 20:17:16,677 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 20:17:16,677 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 20:17:16,677 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 20:17:16,677 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 20:17:16,677 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 20:17:16,677 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 20:17:24,688 - agents.orchestrator - INFO - Initializing Orchestrator with mock (tinyllama) 2026-01-24 20:17:24,690 - agents.orchestrator - INFO - Processing Query: Fetch employee details where department is AI. 2026-01-24 20:17:24,690 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 20:17:24,690 - agents.planner_agent - INFO - Planner analyzing: Fetch employee details where department is AI. 2026-01-24 20:17:24,690 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 20:17:24,690 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 20:17:24,692 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 20:17:24,692 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 20:17:24,692 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 20:17:24,692 - agents.executor_agent - INFO - Tool: get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 20:17:24,692 - agents.executor_agent - INFO - Calling tool: get_employees_by_department 2026-01-24 20:17:24,692 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 20:17:25,016 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 20:17:25,018 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 20:17:25,018 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 20:17:25,018 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 20:17:25,018 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 20:17:25,018 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 20:17:25,018 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 20:17:25,023 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 20:17:25,024 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 20:17:25,024 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 20:17:25,024 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 20:17:25,024 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 20:17:25,025 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 20:17:25,025 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 20:17:25,025 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 20:17:25,025 - agents.executor_agent - INFO - Tool: get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 20:17:25,025 - agents.executor_agent - INFO - Calling tool: get_employees_by_department 2026-01-24 20:17:25,025 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 20:17:25,196 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 20:17:25,196 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 20:17:25,196 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 20:17:25,196 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 20:17:25,197 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 20:17:25,197 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 20:17:25,197 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 21:03:06,756 - agents.orchestrator - INFO - Initializing Orchestrator with mock (tinyllama) 2026-01-24 21:03:06,756 - agents.orchestrator - INFO - Processing Query: Fetch employee details where department is AI. 2026-01-24 21:03:06,756 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 21:03:06,756 - agents.planner_agent - INFO - Planner analyzing: Fetch employee details where department is AI. 2026-01-24 21:03:06,756 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:03:06,756 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 21:03:06,759 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 21:03:06,759 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 21:03:06,759 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 21:03:06,759 - agents.executor_agent - INFO - Tool: get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 21:03:06,759 - agents.executor_agent - INFO - Calling tool: get_employees_by_department 2026-01-24 21:03:06,759 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 21:03:06,825 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 21:03:06,825 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 21:03:06,825 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 21:03:06,825 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 21:03:06,826 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:03:06,826 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 21:03:06,826 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 21:03:06,826 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 21:03:06,826 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 21:03:06,826 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 21:03:06,826 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:03:06,826 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 21:03:06,826 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 21:03:06,827 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 21:03:06,827 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 21:03:06,827 - agents.executor_agent - INFO - Tool: get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 21:03:06,827 - agents.executor_agent - INFO - Calling tool: get_employees_by_department 2026-01-24 21:03:06,827 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 21:03:06,880 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 21:03:06,880 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 21:03:06,880 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 21:03:06,880 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 21:03:06,880 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:03:06,880 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 21:03:06,880 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 21:03:59,463 - agents.orchestrator - INFO - Initializing Orchestrator with mock (tinyllama) 2026-01-24 21:03:59,463 - agents.orchestrator - INFO - Processing Query: Fetch employee details where department is AI. 2026-01-24 21:03:59,463 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 21:03:59,464 - agents.planner_agent - INFO - Planner analyzing: Fetch employee details where department is AI. 2026-01-24 21:03:59,464 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:03:59,464 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 21:03:59,464 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 21:03:59,464 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 21:03:59,464 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 21:03:59,464 - agents.executor_agent - INFO - Tool: get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 21:03:59,464 - agents.executor_agent - INFO - Calling tool: get_employees_by_department 2026-01-24 21:03:59,464 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 21:03:59,512 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 21:03:59,512 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 21:03:59,512 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 21:03:59,512 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 21:03:59,512 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:03:59,512 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 21:03:59,512 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 21:03:59,512 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 21:03:59,513 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 21:03:59,513 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 21:03:59,513 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:03:59,513 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 21:03:59,513 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 21:03:59,513 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 21:03:59,513 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 21:03:59,513 - agents.executor_agent - INFO - Tool: get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 21:03:59,513 - agents.executor_agent - INFO - Calling tool: get_employees_by_department 2026-01-24 21:03:59,513 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 21:03:59,661 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 21:03:59,661 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 21:03:59,661 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 21:03:59,661 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 21:03:59,661 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:03:59,661 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 21:03:59,661 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 21:05:21,273 - agents.orchestrator - INFO - Initializing Orchestrator with mock (tinyllama) 2026-01-24 21:05:21,274 - agents.orchestrator - INFO - Processing Query: Fetch employee details where department is AI. 2026-01-24 21:05:21,274 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 21:05:21,274 - agents.planner_agent - INFO - Planner analyzing: Fetch employee details where department is AI. 2026-01-24 21:05:21,274 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:05:21,274 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 21:05:21,274 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 21:05:21,275 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 21:05:21,275 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 21:05:21,275 - agents.executor_agent - INFO - Tool: get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 21:05:21,275 - agents.executor_agent - INFO - Calling tool: get_employees_by_department 2026-01-24 21:05:21,275 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 21:05:21,370 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 21:05:21,370 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 21:05:21,370 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 21:05:21,371 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 21:05:21,372 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:05:21,372 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 21:05:21,372 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 21:05:21,373 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 21:05:21,373 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 21:05:21,373 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 21:05:21,373 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:05:21,374 - agents.planner_agent - INFO - LLM Response: {"tool": "get_projects_by_status", "parameters": {"status": "In Progress"}, "reasoning": "User asked for projects in progress."}... 2026-01-24 21:05:21,374 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_projects_by_status', 'parameters': {'status': 'In Progress'}, 'reasoning': 'User asked for projects in progress.'} 2026-01-24 21:05:21,374 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 21:05:21,375 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_projects_by_status', 'parameters': {'status': 'In Progress'}, 'reasoning': 'User asked for projects in progress.'} 2026-01-24 21:05:21,375 - agents.executor_agent - INFO - Tool: get_projects_by_status, Parameters: {'status': 'In Progress'} 2026-01-24 21:05:21,375 - agents.executor_agent - INFO - Calling tool: get_projects_by_status 2026-01-24 21:05:21,375 - mcp.tools - INFO - Tool called: get_projects_by_status with input=In Progress 2026-01-24 21:05:21,476 - database.db_executor - INFO - Found 2 projects with status In Progress 2026-01-24 21:05:21,476 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 2 rows 2026-01-24 21:05:21,476 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 21:05:21,476 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 21:05:21,476 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:05:21,476 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 21:05:21,476 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 21:13:53,788 - agents.orchestrator - INFO - Initializing Orchestrator with mock (tinyllama) 2026-01-24 21:13:53,788 - agents.orchestrator - INFO - Processing Query: Fetch employee details where department is AI. 2026-01-24 21:13:53,788 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 21:13:53,788 - agents.planner_agent - INFO - Planner analyzing: Fetch employee details where department is AI. 2026-01-24 21:13:53,788 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:13:53,788 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 21:13:53,788 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 21:13:53,788 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 21:13:53,788 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 21:13:53,788 - agents.executor_agent - INFO - Tool: get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 21:13:53,788 - agents.executor_agent - INFO - Calling tool: get_employees_by_department 2026-01-24 21:13:53,788 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 21:13:53,846 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 21:13:53,846 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 21:13:53,846 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 21:13:53,846 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 21:13:53,846 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:13:53,846 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 21:13:53,846 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 21:13:53,847 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 21:13:53,847 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 21:13:53,847 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 21:13:53,847 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:13:53,847 - agents.planner_agent - INFO - LLM Response: {"tool": "get_projects_by_status", "parameters": {"status": "In Progress"}, "reasoning": "User asked for projects in progress."}... 2026-01-24 21:13:53,847 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_projects_by_status', 'parameters': {'status': 'In Progress'}, 'reasoning': 'User asked for projects in progress.'} 2026-01-24 21:13:53,847 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 21:13:53,847 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_projects_by_status', 'parameters': {'status': 'In Progress'}, 'reasoning': 'User asked for projects in progress.'} 2026-01-24 21:13:53,847 - agents.executor_agent - INFO - Tool: get_projects_by_status, Parameters: {'status': 'In Progress'} 2026-01-24 21:13:53,847 - agents.executor_agent - INFO - Calling tool: get_projects_by_status 2026-01-24 21:13:53,847 - mcp.tools - INFO - Tool called: get_projects_by_status with input=In Progress 2026-01-24 21:13:53,894 - database.db_executor - INFO - Found 2 projects with status In Progress 2026-01-24 21:13:53,894 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 2 rows 2026-01-24 21:13:53,894 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 21:13:53,894 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 21:13:53,894 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:13:53,894 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 21:13:53,894 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 21:16:22,034 - agents.orchestrator - INFO - Initializing Orchestrator with mock (tinyllama) 2026-01-24 21:16:22,034 - agents.orchestrator - INFO - Processing Query: Fetch employee details where department is AI. 2026-01-24 21:16:22,034 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 21:16:22,035 - agents.planner_agent - INFO - Planner analyzing: Fetch employee details where department is AI. 2026-01-24 21:16:22,035 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:16:22,035 - agents.planner_agent - INFO - LLM Response: {"tool": "get_employees_by_department", "parameters": {"department": "AI"}, "reasoning": "User asked for employees in AI department."}... 2026-01-24 21:16:22,035 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 21:16:22,035 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 21:16:22,035 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_employees_by_department', 'parameters': {'department': 'AI'}, 'reasoning': 'User asked for employees in AI department.'} 2026-01-24 21:16:22,035 - agents.executor_agent - INFO - Tool: get_employees_by_department, Parameters: {'department': 'AI'} 2026-01-24 21:16:22,035 - agents.executor_agent - INFO - Calling tool: get_employees_by_department 2026-01-24 21:16:22,035 - mcp.tools - INFO - Tool called: get_employees_by_department with input=AI 2026-01-24 21:16:22,137 - database.db_executor - INFO - Found 3 employees in AI department 2026-01-24 21:16:22,137 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 3 rows 2026-01-24 21:16:22,137 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 21:16:22,137 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 21:16:22,137 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:16:22,137 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 21:16:22,137 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 21:16:22,138 - agents.orchestrator - INFO - Processing Query: What are the projects that are in progress? 2026-01-24 21:16:22,139 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 21:16:22,139 - agents.planner_agent - INFO - Planner analyzing: What are the projects that are in progress? 2026-01-24 21:16:22,139 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:16:22,139 - agents.planner_agent - INFO - LLM Response: {"tool": "get_projects_by_status", "parameters": {"status": "In Progress"}, "reasoning": "User asked for projects in progress."}... 2026-01-24 21:16:22,139 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_projects_by_status', 'parameters': {'status': 'In Progress'}, 'reasoning': 'User asked for projects in progress.'} 2026-01-24 21:16:22,139 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 21:16:22,139 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_projects_by_status', 'parameters': {'status': 'In Progress'}, 'reasoning': 'User asked for projects in progress.'} 2026-01-24 21:16:22,140 - agents.executor_agent - INFO - Tool: get_projects_by_status, Parameters: {'status': 'In Progress'} 2026-01-24 21:16:22,140 - agents.executor_agent - INFO - Calling tool: get_projects_by_status 2026-01-24 21:16:22,140 - mcp.tools - INFO - Tool called: get_projects_by_status with input=In Progress 2026-01-24 21:16:22,232 - database.db_executor - INFO - Found 2 projects with status In Progress 2026-01-24 21:16:22,232 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 2 rows 2026-01-24 21:16:22,232 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 21:16:22,232 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 21:16:22,232 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:16:22,232 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 21:16:22,232 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 21:16:22,235 - agents.orchestrator - INFO - Processing Query: What are the high priority issues? 2026-01-24 21:16:22,235 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 21:16:22,235 - agents.planner_agent - INFO - Planner analyzing: What are the high priority issues? 2026-01-24 21:16:22,235 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:16:22,235 - agents.planner_agent - INFO - LLM Response: {"tool": "get_issues_by_priority", "parameters": {"priority": "High"}, "reasoning": "User asked for high priority issues."}... 2026-01-24 21:16:22,235 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_issues_by_priority', 'parameters': {'priority': 'High'}, 'reasoning': 'User asked for high priority issues.'} 2026-01-24 21:16:22,235 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 21:16:22,235 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_issues_by_priority', 'parameters': {'priority': 'High'}, 'reasoning': 'User asked for high priority issues.'} 2026-01-24 21:16:22,235 - agents.executor_agent - INFO - Tool: get_issues_by_priority, Parameters: {'priority': 'High'} 2026-01-24 21:16:22,235 - agents.executor_agent - INFO - Calling tool: get_issues_by_priority 2026-01-24 21:16:22,235 - mcp.tools - INFO - Tool called: get_issues_by_priority with input=High 2026-01-24 21:16:22,336 - database.db_executor - INFO - Found 2 issues with priority High 2026-01-24 21:16:22,336 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 2 rows 2026-01-24 21:16:22,336 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 21:16:22,337 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 21:16:22,337 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:16:22,337 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 21:16:22,337 - agents.orchestrator - INFO - ✅ Query Processing Complete 2026-01-24 21:16:22,338 - agents.orchestrator - INFO - Processing Query: What are the completed projects? 2026-01-24 21:16:22,338 - agents.orchestrator - INFO - [1/3] Planning... 2026-01-24 21:16:22,338 - agents.planner_agent - INFO - Planner analyzing: What are the completed projects? 2026-01-24 21:16:22,339 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:16:22,339 - agents.planner_agent - INFO - LLM Response: {"tool": "get_projects_by_status", "parameters": {"status": "Completed"}, "reasoning": "User asked for completed projects."}... 2026-01-24 21:16:22,340 - agents.planner_agent - INFO - ✅ Plan: {'tool': 'get_projects_by_status', 'parameters': {'status': 'Completed'}, 'reasoning': 'User asked for completed projects.'} 2026-01-24 21:16:22,340 - agents.orchestrator - INFO - [2/3] Executing... 2026-01-24 21:16:22,340 - agents.executor_agent - INFO - Executing plan: {'tool': 'get_projects_by_status', 'parameters': {'status': 'Completed'}, 'reasoning': 'User asked for completed projects.'} 2026-01-24 21:16:22,340 - agents.executor_agent - INFO - Tool: get_projects_by_status, Parameters: {'status': 'Completed'} 2026-01-24 21:16:22,340 - agents.executor_agent - INFO - Calling tool: get_projects_by_status 2026-01-24 21:16:22,340 - mcp.tools - INFO - Tool called: get_projects_by_status with input=Completed 2026-01-24 21:16:22,434 - database.db_executor - INFO - Found 1 projects with status Completed 2026-01-24 21:16:22,435 - agents.executor_agent - INFO - ✅ Tool executed successfully, got 1 rows 2026-01-24 21:16:22,435 - agents.orchestrator - INFO - [3/3] 🤔 Reasoning... 2026-01-24 21:16:22,435 - agents.reasoner_agent - INFO - 🤔 Reasoning about results... 2026-01-24 21:16:22,435 - agents.llm_provider - INFO - Calling Mock Provider (simulating tinyllama)... 2026-01-24 21:16:22,435 - agents.reasoner_agent - INFO - ✅ Reasoning complete 2026-01-24 21:16:22,435 - agents.orchestrator - INFO - ✅ Query Processing Complete