EX MCP Server

# MCP Tool Sweep Report - Using DEFAULT_MODEL=glm-4.5-flash - Providers: KIMI=set, GLM=set - Initialized server: exai v5.8.5 - Tools discovered (7): analyze, challenge, chat, consensus, listmodels, thinkdeep, version ## Tool: analyze <details><summary>Input Schema</summary> ```json { "$schema": "http://json-schema.org/draft-07/schema#", "type": "object", "properties": { "step": { "type": "string", "description": "What to analyze or look for in this step. In step 1, describe what you want to analyze and begin forming an analytical approach after thinking carefully about what needs to be examined. Consider code quality, performance implications, architectural patterns, and design decisions. Map out the codebase structure, understand the business logic, and identify areas requiring deeper analysis. In later steps, continue exploring with precision and adapt your understanding as you uncover more insights." }, "step_number": { "type": "integer", "minimum": 1, "description": "The index of the current step in the analysis sequence, beginning at 1. Each step should build upon or revise the previous one." }, "total_steps": { "type": "integer", "minimum": 1, "description": "Your current estimate for how many steps will be needed to complete the analysis. Adjust as new findings emerge." }, "next_step_required": { "type": "boolean", "description": "Set to true if you plan to continue the investigation with another step. False means you believe the analysis is complete and ready for expert validation." }, "findings": { "type": "string", "description": "Summarize everything discovered in this step about the code being analyzed. Include analysis of architectural patterns, design decisions, tech stack assessment, scalability characteristics, performance implications, maintainability factors, security posture, and strategic improvement opportunities. Be specific and avoid vague language\u2014document what you now know about the codebase and how it affects your assessment. IMPORTANT: Document both strengths (good patterns, solid architecture, well-designed components) and concerns (tech debt, scalability risks, overengineering, unnecessary complexity). In later steps, confirm or update past findings with additional evidence." }, "files_checked": { "type": "array", "items": { "type": "string" }, "description": "List all files (as absolute paths, do not clip or shrink file names) examined during the analysis investigation so far. Include even files ruled out or found to be unrelated, as this tracks your exploration path." }, "relevant_files": { "type": "array", "items": { "type": "string" }, "description": "Subset of files_checked (as full absolute paths) that contain code directly relevant to the analysis or contain significant patterns, architectural decisions, or examples worth highlighting. Only list those that are directly tied to important findings, architectural insights, performance characteristics, or strategic improvement opportunities. This could include core implementation files, configuration files, or files demonstrating key patterns." }, "relevant_context": { "type": "array", "items": { "type": "string" }, "description": "Methods/functions identified as involved in the issue" }, "issues_found": { "type": "array", "items": { "type": "object" }, "description": "Issues or concerns identified during analysis, each with severity level (critical, high, medium, low)" }, "backtrack_from_step": { "type": "integer", "minimum": 1, "description": "If an earlier finding or assessment needs to be revised or discarded, specify the step number from which to start over. Use this to acknowledge investigative dead ends and correct the course." }, "use_assistant_model": { "type": "boolean", "default": true, "description": "Whether to use assistant model for expert analysis after completing the workflow steps. Set to False to skip expert analysis and rely solely on Claude's investigation. Defaults to True for comprehensive validation." }, "temperature": { "type": "number", "description": "Temperature for response (0.0 to 1.0). Lower values are more focused and deterministic, higher values are more creative. Tool-specific defaults apply if not specified.", "minimum": 0.0, "maximum": 1.0 }, "thinking_mode": { "type": "string", "enum": [ "minimal", "low", "medium", "high", "max" ], "description": "Thinking depth: minimal (0.5% of model max), low (8%), medium (33%), high (67%), max (100% of model max). Higher modes enable deeper reasoning at the cost of speed." }, "use_websearch": { "type": "boolean", "description": "Enable web search for documentation, best practices, and current information. When enabled, the model can request Claude to perform web searches and share results back during conversations. Particularly useful for: brainstorming sessions, architectural design discussions, exploring industry best practices, working with specific frameworks/technologies, researching solutions to complex problems, or when current documentation and community insights would enhance the analysis.", "default": true }, "continuation_id": { "type": "string", "description": "Thread continuation ID for multi-turn conversations. When provided, the complete conversation history is automatically embedded as context. Your response should build upon this history without repeating previous analysis or instructions. Focus on providing only new insights, additional findings, or answers to follow-up questions. Can be used across different tools." }, "images": { "type": "array", "items": { "type": "string" }, "description": "Optional list of absolute paths to architecture diagrams, design documents, or visual references that help with analysis context. Only include if they materially assist understanding or assessment." }, "model": { "type": "string", "description": "Model to use. Native models: 'auto', 'glm-4.5', 'glm-4.5-air', 'glm-4.5-flash', 'kimi-latest', 'moonshot-v1-128k', 'moonshot-v1-32k', 'moonshot-v1-8k'. Use 'auto' to let the server select the best model. Defaults to 'glm-4.5-flash' if not specified." }, "confidence": { "type": "string", "enum": [ "exploring", "low", "medium", "high", "very_high", "almost_certain", "certain" ], "description": "Your confidence level in the current analysis findings: exploring (early investigation), low (some insights but more needed), medium (solid understanding), high (comprehensive insights), very_high (very comprehensive insights), almost_certain (nearly complete analysis), certain (100% confidence - complete analysis ready for expert validation)" }, "analysis_type": { "type": "string", "enum": [ "architecture", "performance", "security", "quality", "general" ], "default": "general", "description": "Type of analysis to perform (architecture, performance, security, quality, general). Synonyms like 'comprehensive' will map to 'general'." }, "output_format": { "type": "string", "enum": [ "summary", "detailed", "actionable" ], "default": "detailed", "description": "How to format the output (summary, detailed, actionable)" } }, "required": [ "step", "step_number", "total_steps", "next_step_required", "findings" ], "additionalProperties": false, "title": "AnalyzeRequest" } ``` </details> Request: ```json { "step": "Automated test step", "step_number": 1, "total_steps": 1, "next_step_required": false, "findings": "Automated test step", "relevant_files": [ "C:\\Project\\EX-AI-MCP-Server\\README.md" ], "use_assistant_model": false, "model": "auto" } ``` Result: SUCCESS Resolved: provider=kimi, model=kimi-thinking-preview - Duration: 0.03s ```text Activity: 4 progress events (req_id=a7b538c7-a988-442c-8bd5-187205d60c3c) === PROGRESS === [PROGRESS] analyze: Starting step 1/1 - Automated test step [PROGRESS] analyze: Processed step data. Updating findings... [PROGRESS] analyze: Finalizing - calling expert analysis if required... [PROGRESS] analyze: Step 1/1 complete === END PROGRESS === req_id=a7b538c7-a988-442c-8bd5-187205d60c3c {"status": "local_work_complete", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "7de13845-8ea2-4cb2-a664-ce092716d372", "file_context": {"type": "fully_embedded", "files_embedded": 1, "context_optimization": "Full file content embedded for expert analysis"}, "next_steps": "Local analyze complete with sufficient confidence. Present findings and recommendations to the user based on the work results.", "analysis_status": {"files_checked": 0, "relevant_files": 1, "relevant_context": 0, "issues_found": 0, "images_collected": 0, "current_confidence": "low", "insights_by_severity": {}, "analysis_confidence": "low"}, "analysis_complete": true, "metadata": {"tool_name": "analyze", "model_used": "kimi-thinking-preview", "provider_used": "kimi", "progress": ["analyze: Starting step 1/1 - Automated test step", "analyze: Processed step data. Updating findings...", "analyze: Finalizing - calling expert analysis if required...", "analyze: Step 1/1 complete"]}, "progress_text": "[PROGRESS] analyze: Starting step 1/1 - Automated test step\n[PROGRESS] analyze: Processed step data. Updating findings...\n[PROGRESS] analyze: Finalizing - calling expert analysis if required...\n[PROGRESS] analyze: Step 1/1 complete"} ``` ## Tool: challenge <details><summary>Input Schema</summary> ```json { "type": "object", "properties": { "prompt": { "type": "string", "description": "The user's message or statement to analyze critically. When manually invoked with 'challenge', exclude that prefix - just pass the actual content. For automatic invocations (see tool description for conditions), pass the user's complete message unchanged." } }, "required": [ "prompt" ] } ``` </details> Request: ```json { "prompt": "Hello from MCP tool sweep test." } ``` Result: SUCCESS - Duration: 0.01s ```text {"original_statement": "Hello from MCP tool sweep test.", "challenge_prompt": "CRITICAL REASSESSMENT – Do not automatically agree:\n\n\"Hello from MCP tool sweep test.\"\n\nCarefully evaluate the statement above. Is it accurate, complete, and well-reasoned? Investigate if needed before replying, and stay focused. If you identify flaws, gaps, or misleading points, explain them clearly. Likewise, if you find the reasoning sound, explain why it holds up. Respond with thoughtful analysis—stay to the point and avoid reflexive agreement.", "instructions": "Present the challenge_prompt to yourself and follow its instructions. Reassess the statement carefully and critically before responding. If, after reflection, you find reasons to disagree or qualify it, explain your reasoning. Likewise, if you find reasons to agree, articulate them clearly and justify your agreement."} ``` ## Tool: chat <details><summary>Input Schema</summary> ```json { "type": "object", "properties": { "prompt": { "type": "string", "description": "You MUST provide a thorough, expressive question or share an idea with as much context as possible. IMPORTANT: When referring to code, use the files parameter to pass relevant files and only use the prompt to refer to function / method names or very small code snippets if absolutely necessary to explain the issue. Do NOT pass large code snippets in the prompt as this is exclusively reserved for descriptive text only. Remember: you're talking to an assistant who has deep expertise and can provide nuanced insights. Include your current thinking, specific challenges, background context, what you've already tried, and what kind of response would be most helpful. The more context and detail you provide, the more valuable and targeted the response will be." }, "files": { "type": "array", "items": { "type": "string" }, "description": "Optional files for context (must be FULL absolute paths to real files / folders - DO NOT SHORTEN)" }, "images": { "type": "array", "items": { "type": "string" }, "description": "Optional images for visual context. Useful for UI discussions, diagrams, visual problems, error screens, or architectural mockups. (must be FULL absolute paths to real files / folders - DO NOT SHORTEN - OR these can be base64 data)" }, "model": { "type": "string", "description": "Model to use. Native models: 'auto', 'glm-4.5', 'glm-4.5-air', 'glm-4.5-flash', 'kimi-latest', 'moonshot-v1-128k', 'moonshot-v1-32k', 'moonshot-v1-8k'. Use 'auto' to let the server select the best model. Defaults to 'glm-4.5-flash' if not specified." }, "temperature": { "type": "number", "description": "Response creativity (0-1, default 0.5)", "minimum": 0, "maximum": 1 }, "thinking_mode": { "type": "string", "enum": [ "minimal", "low", "medium", "high", "max" ], "description": "Thinking depth: minimal (0.5% of model max), low (8%), medium (33%), high (67%), max (100% of model max)" }, "use_websearch": { "type": "boolean", "description": "Enable web search for documentation, best practices, and current information. Particularly useful for: brainstorming sessions, architectural design discussions, exploring industry best practices, working with specific frameworks/technologies, researching solutions to complex problems, or when current documentation and community insights would enhance the analysis.", "default": true }, "continuation_id": { "type": "string", "description": "Thread continuation ID for multi-turn conversations. Can be used to continue conversations across different tools. Only provide this if continuing a previous conversation thread." } }, "required": [ "prompt" ] } ``` </details> Request: ```json { "prompt": "Hello from MCP tool sweep test.", "model": "auto" } ``` Result: SUCCESS Resolved: provider=glm, model=glm-4.5-air - Duration: 4.50s ```text === PROGRESS === [PROGRESS] chat: Starting execution [PROGRESS] chat: Request validated [PROGRESS] chat: Model/context ready: glm-4.5-flash [PROGRESS] chat: Generating response (~1,954 tokens) === END PROGRESS === Hello! I'm ready to collaborate as your senior engineering thought-partner. When you have technical code, architecture decisions, or implementation challenges to discuss, I'll be here to help brainstorm solutions, validate approaches, and provide well-reasoned perspectives grounded in practical engineering considerations. Feel free to share any code snippets, technical questions, or design challenges you'd like to explore together. I'm particularly focused on keeping solutions practical and within scope - no overengineering unless clearly justified. What technical topic would you like to dive into? --- AGENT'S TURN: Evaluate this perspective alongside your analysis to form a comprehensive solution and continue with the user's request and task at hand. ``` ## Tool: consensus <details><summary>Input Schema</summary> ```json { "$schema": "http://json-schema.org/draft-07/schema#", "type": "object", "properties": { "step": { "type": "string", "description": "In step 1: Provide the EXACT question or proposal that ALL models will evaluate. This should be phrased as a clear question or problem statement, NOT as 'I will analyze...' or 'Let me examine...'. For example: 'Should we build a search component in SwiftUI for use in an AppKit app?' or 'Evaluate the proposal to migrate our database from MySQL to PostgreSQL'. This exact text will be sent to all models for their independent evaluation. In subsequent steps (2+): This field is for internal tracking only - you can provide notes about the model response you just received. This will NOT be sent to other models (they all receive the original proposal from step 1)." }, "step_number": { "type": "integer", "minimum": 1, "description": "The index of the current step in the consensus workflow, beginning at 1. Step 1 is your analysis, steps 2+ are for processing individual model responses." }, "total_steps": { "type": "integer", "minimum": 1, "description": "Total number of steps needed. This equals the number of models to consult. Step 1 includes your analysis + first model consultation on return of the call. Final step includes last model consultation + synthesis." }, "next_step_required": { "type": "boolean", "description": "Set to true if more models need to be consulted. False when ready for final synthesis." }, "findings": { "type": "string", "description": "In step 1: Provide YOUR OWN comprehensive analysis of the proposal/question. This is where you share your independent evaluation, considering technical feasibility, risks, benefits, and alternatives. This analysis is NOT sent to other models - it's recorded for the final synthesis. In steps 2+: Summarize the key points from the model response received, noting agreements and disagreements with previous analyses." }, "relevant_files": { "type": "array", "items": { "type": "string" }, "description": "Files that are relevant to the consensus analysis. Include files that help understand the proposal, provide context, or contain implementation details." }, "use_assistant_model": { "type": "boolean", "default": true, "description": "Whether to use assistant model for expert analysis after completing the workflow steps. Set to False to skip expert analysis and rely solely on Claude's investigation. Defaults to True for comprehensive validation." }, "continuation_id": { "type": "string", "description": "Thread continuation ID for multi-turn conversations. When provided, the complete conversation history is automatically embedded as context. Your response should build upon this history without repeating previous analysis or instructions. Focus on providing only new insights, additional findings, or answers to follow-up questions. Can be used across different tools." }, "images": { "type": "array", "items": { "type": "string" }, "description": "Optional list of image paths or base64 data URLs for visual context. Useful for UI/UX discussions, architecture diagrams, mockups, or any visual references that help inform the consensus analysis." }, "model": { "type": "string", "description": "Model to use. Native models: 'auto', 'glm-4.5', 'glm-4.5-air', 'glm-4.5-flash', 'kimi-latest', 'moonshot-v1-128k', 'moonshot-v1-32k', 'moonshot-v1-8k'. Use 'auto' to let the server select the best model. Defaults to 'glm-4.5-flash' if not specified." }, "models": { "type": "array", "items": { "type": "object", "properties": { "model": { "type": "string" }, "stance": { "type": "string", "enum": [ "for", "against", "neutral" ], "default": "neutral" }, "stance_prompt": { "type": "string" } }, "required": [ "model" ] }, "description": "List of model configurations to consult. Each can have a model name, stance (for/against/neutral), and optional custom stance prompt. The same model can be used multiple times with different stances, but each model + stance combination must be unique. Example: [{'model': 'o3', 'stance': 'for'}, {'model': 'o3', 'stance': 'against'}, {'model': 'flash', 'stance': 'neutral'}]" }, "current_model_index": { "type": "integer", "minimum": 0, "description": "Internal tracking of which model is being consulted (0-based index). Used to determine which model to call next." }, "model_responses": { "type": "array", "items": { "type": "object" }, "description": "Accumulated responses from models consulted so far. Internal field for tracking progress." } }, "required": [ "step", "step_number", "total_steps", "next_step_required", "findings" ], "additionalProperties": false, "title": "ConsensusRequest" } ``` </details> Request: ```json { "step": "Automated test step", "step_number": 1, "total_steps": 1, "next_step_required": false, "findings": "Automated test step", "relevant_files": [ "C:\\Project\\EX-AI-MCP-Server\\README.md" ], "use_assistant_model": false, "model": "auto" } ``` Result: SUCCESS - Duration: 0.01s ```text {"status": "invalid_request", "error": "Invalid arguments for tool", "details": "1 validation error for ConsensusRequest\n Value error, Step 1 requires 'models' field to specify which models to consult [type=value_error, input_value={'step': 'Automated test ...'kimi-thinking-preview'}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.11/v/value_error", "tool": "consensus"} ``` ## Tool: listmodels <details><summary>Input Schema</summary> ```json { "type": "object", "properties": { "model": { "type": "string", "description": "Model to use (ignored by listmodels tool)" } }, "required": [] } ``` </details> Request: ```json { "model": "auto" } ``` Result: SUCCESS - Duration: 0.01s ```text # Available AI Models ## Moonshot Kimi ✅ **Status**: Configured and available **Models**: - `kimi-k2-0905-preview` - 128K context - `kimi-k2-0711-preview` - 128K context - `moonshot-v1-8k` - 8K context - `moonshot-v1-32k` - 32K context - `kimi-k2-turbo-preview` - 256K context - `moonshot-v1-128k` - 128K context - `moonshot-v1-8k-vision-preview` - 8K context - `moonshot-v1-32k-vision-preview` - 32K context - `moonshot-v1-128k-vision-preview` - 128K context - `kimi-latest` - 128K context - `kimi-thinking-preview` - 128K context **Aliases**: - `kimi-k2-0711` → `kimi-k2-0711-preview` - `kimi-k2-0905` → `kimi-k2-0905-preview` - `kimi-k2-turbo` → `kimi-k2-turbo-preview` - `kimi-k2` → `kimi-k2-0905-preview` ## ZhipuAI GLM ✅ **Status**: Configured and available **Models**: - `glm-4.5-flash` - 128K context - `glm-4.5` - 128K context - `glm-4.5-air` - 128K context **Aliases**: - `glm-4.5-air` → `glm-4.5-flash` ## OpenRouter ❌ **Status**: Not configured (set OPENROUTER_API_KEY) **Note**: Provides access to GPT-5, O3, Mistral, and many more ## Custom/Local API ❌ **Status**: Not configured (set CUSTOM_API_URL) **Example**: CUSTOM_API_URL=http://localhost:11434 (for Ollama) ## Summary **Configured Providers**: 2 **Total Available Models**: 18 **Usage Tips**: - Use model aliases (e.g., 'flash', 'gpt5', 'opus') for convenience - In auto mode, the CLI Agent will select the best model for each task - Custom models are only available when CUSTOM_API_URL is set - OpenRouter provides access to many cloud models with one API key ``` ## Tool: thinkdeep <details><summary>Input Schema</summary> ```json { "$schema": "http://json-schema.org/draft-07/schema#", "type": "object", "properties": { "step": { "type": "string", "description": "Current work step content and findings from your overall work" }, "step_number": { "type": "integer", "minimum": 1, "description": "Current step number in the work sequence (starts at 1)" }, "total_steps": { "type": "integer", "minimum": 1, "description": "Estimated total steps needed to complete the work" }, "next_step_required": { "type": "boolean", "description": "Whether another work step is needed after this one" }, "findings": { "type": "string", "description": "Important findings, evidence and insights discovered in this step of the work" }, "files_checked": { "type": "array", "items": { "type": "string" }, "description": "List of files examined during this work step" }, "relevant_files": { "type": "array", "items": { "type": "string" }, "description": "Files identified as relevant to the issue/goal" }, "relevant_context": { "type": "array", "items": { "type": "string" }, "description": "Methods/functions identified as involved in the issue" }, "issues_found": { "type": "array", "items": { "type": "object" }, "description": "Issues identified with severity levels during work" }, "confidence": { "type": "string", "enum": [ "exploring", "low", "medium", "high", "very_high", "almost_certain", "certain" ], "description": "Confidence level in findings: exploring (just starting), low (early investigation), medium (some evidence), high (strong evidence), very_high (comprehensive understanding), almost_certain (near complete confidence), certain (100% confidence locally - no external validation needed)" }, "hypothesis": { "type": "string", "description": "Current theory about the issue/goal based on work" }, "backtrack_from_step": { "type": "integer", "minimum": 1, "description": "Step number to backtrack from if work needs revision" }, "use_assistant_model": { "type": "boolean", "default": true, "description": "Whether to use assistant model for expert analysis after completing the workflow steps. Set to False to skip expert analysis and rely solely on Claude's investigation. Defaults to True for comprehensive validation." }, "temperature": { "type": "number", "description": "Temperature for response (0.0 to 1.0). Lower values are more focused and deterministic, higher values are more creative. Tool-specific defaults apply if not specified.", "minimum": 0.0, "maximum": 1.0 }, "thinking_mode": { "type": "string", "enum": [ "minimal", "low", "medium", "high", "max" ], "description": "Thinking depth: minimal (0.5% of model max), low (8%), medium (33%), high (67%), max (100% of model max). Higher modes enable deeper reasoning at the cost of speed." }, "use_websearch": { "type": "boolean", "description": "Enable web search for documentation, best practices, and current information. When enabled, the model can request Claude to perform web searches and share results back during conversations. Particularly useful for: brainstorming sessions, architectural design discussions, exploring industry best practices, working with specific frameworks/technologies, researching solutions to complex problems, or when current documentation and community insights would enhance the analysis.", "default": true }, "continuation_id": { "type": "string", "description": "Thread continuation ID for multi-turn conversations. When provided, the complete conversation history is automatically embedded as context. Your response should build upon this history without repeating previous analysis or instructions. Focus on providing only new insights, additional findings, or answers to follow-up questions. Can be used across different tools." }, "images": { "type": "array", "items": { "type": "string" }, "description": "Optional image(s) for visual context. Accepts absolute file paths or base64 data URLs. Only provide when user explicitly mentions images. When including images, please describe what you believe each image contains to aid with contextual understanding. Useful for UI discussions, diagrams, visual problems, error screens, architecture mockups, and visual analysis tasks." }, "model": { "type": "string", "description": "Model to use. Native models: 'auto', 'glm-4.5', 'glm-4.5-air', 'glm-4.5-flash', 'kimi-latest', 'moonshot-v1-128k', 'moonshot-v1-32k', 'moonshot-v1-8k'. Use 'auto' to let the server select the best model. Defaults to 'glm-4.5-flash' if not specified." }, "problem_context": { "type": "string", "description": "Provide additional context about the problem or goal. Be as expressive as possible. More information will be very helpful for the analysis." }, "focus_areas": { "type": "array", "items": { "type": "string" }, "description": "Specific aspects to focus on (architecture, performance, security, etc.)" } }, "required": [ "step", "step_number", "total_steps", "next_step_required", "findings" ], "additionalProperties": false, "title": "ThinkdeepRequest" } ``` </details> Request: ```json { "step": "Automated test step", "step_number": 1, "total_steps": 1, "next_step_required": false, "findings": "Automated test step", "relevant_files": [ "C:\\Project\\EX-AI-MCP-Server\\README.md" ], "use_assistant_model": false, "model": "auto" } ``` Result: SUCCESS Resolved: provider=kimi, model=kimi-thinking-preview - Duration: 0.02s ```text Activity: 4 progress events (req_id=a6d23ea5-6405-4c96-9a98-54b11fb15669) === PROGRESS === [PROGRESS] thinkdeep: Starting step 1/1 - Automated test step [PROGRESS] thinkdeep: Processed step data. Updating findings... [PROGRESS] thinkdeep: Finalizing - calling expert analysis if required... [PROGRESS] thinkdeep: Step 1/1 complete === END PROGRESS === req_id=a6d23ea5-6405-4c96-9a98-54b11fb15669 {"status": "local_work_complete", "step_number": 1, "total_steps": 1, "next_step_required": false, "thinkdeep_status": {"files_checked": 0, "relevant_files": 1, "relevant_context": 0, "issues_found": 0, "images_collected": 0, "current_confidence": "low"}, "continuation_id": "0c3e697b-03e2-46de-949d-6381d5cec0f9", "file_context": {"type": "fully_embedded", "files_embedded": 1, "context_optimization": "Full file content embedded for expert analysis"}, "thinkdeep_complete": true, "next_steps": "Local thinkdeep complete with sufficient confidence. Present findings and recommendations to the user based on the work results.", "thinking_status": {"current_step": 1, "total_steps": 1, "files_checked": 0, "relevant_files": 1, "thinking_confidence": "low", "analysis_focus": ["general"]}, "thinking_complete": true, "complete_thinking": {"steps_completed": 1, "final_confidence": "low", "relevant_context": [], "key_findings": ["Step 1: Automated test step"], "issues_identified": [], "files_analyzed": ["C:\\Project\\EX-AI-MCP-Server\\README.md"]}, "completion_message": "Deep thinking analysis phase complete. Expert validation will provide additional insights and recommendations.", "metadata": {"tool_name": "thinkdeep", "model_used": "kimi-thinking-preview", "provider_used": "kimi", "progress": ["thinkdeep: Starting step 1/1 - Automated test step", "thinkdeep: Processed step data. Updating findings...", "thinkdeep: Finalizing - calling expert analysis if required...", "thinkdeep: Step 1/1 complete"]}, "progress_text": "[PROGRESS] thinkdeep: Starting step 1/1 - Automated test step\n[PROGRESS] thinkdeep: Processed step data. Updating findings...\n[PROGRESS] thinkdeep: Finalizing - calling expert analysis if required...\n[PROGRESS] thinkdeep: Step 1/1 complete"} ``` ## Tool: version <details><summary>Input Schema</summary> ```json { "type": "object", "properties": { "model": { "type": "string", "description": "Model to use (ignored by version tool)" } }, "required": [] } ``` </details> Request: ```json { "model": "auto" } ``` Result: SUCCESS - Duration: 0.01s ```text # EX MCP Server Version ## Server Information **Current Version**: 5.8.5 **Last Updated**: 2025-08-08 **Author**: Zazzles **Connected Client**: MCP Client **Installation Path**: `C:\Project\EX-AI-MCP-Server` ## Version Source This local build is authoritative. Online update checks are disabled. ## Configuration **Providers**: - **Moonshot Kimi**: ✅ Configured - **ZhipuAI GLM**: ✅ Configured - **Google Gemini**: ❌ Not configured - **OpenAI**: ❌ Not configured - **X.AI**: ❌ Not configured - **DIAL**: ❌ Not configured - **OpenRouter**: ❌ Not configured - **Custom/Local**: ❌ Not configured **Available Models**: 18 ```