Analysis MCP

# analysis-mcp A FastMCP server for critical thinking and multi-perspective analysis of current affairs. **Uses the LLM-Orchestrator Pattern**: Tools return structured prompts for the calling LLM to execute, enabling **iterative complexity building** through prompt chaining. ## 🧠 Core Concept: Prompt Chaining for Complexity Instead of doing one analysis, **chain operations** to build increasingly sophisticated insights: ``` 1. deconstruct_claim("AI will replace jobs") → Get structured breakdown 2. chain_analysis(previous_output, "extract_assumptions") → Find hidden assumptions in your analysis 3. chain_analysis(previous_output, "identify_contradictions") → Spot tensions in the argument 4. chain_analysis(previous_output, "steelman_argument") → Build strongest version of the claim 5. chain_analysis(previous_output, "suggest_next_step") → Get recommendation for deeper analysis ``` Each step builds on the last, creating layered, sophisticated thinking. ## Features **Core Analytical Tools:** - `deconstruct_claim` - Break down claims into components - `compare_positions` - Multi-perspective ideological analysis - `apply_lens` - Analyze through 9 frameworks (historical, economic, etc.) - `get_trace` - Retrieve previous analysis plans **🔗 Prompt Chaining Tools (NEW):** - `apply_operation` - Apply 15+ analytical operations to any content - `chain_analysis` - Chain operations on previous LLM outputs - `list_available_operations` - Browse all available operations **15+ Analytical Operations:** *Deconstructive:* - `extract_assumptions` - Find implicit/explicit assumptions - `identify_contradictions` - Spot logical tensions - `find_fallacies` - Detect rhetorical manipulation *Constructive:* - `steelman_argument` - Build strongest version - `find_analogies` - Identify relevant precedents - `extract_principles` - Derive universal patterns *Synthetic:* - `synthesize_perspectives` - Merge viewpoints - `elevate_abstraction` - Raise to higher concepts - `ground_in_specifics` - Add concrete examples *Meta-analytical:* - `identify_gaps` - Find missing elements - `check_coherence` - Verify logical consistency - `suggest_next_step` - Recommend next operation *Transformative:* - `convert_to_dialogue` - Reframe as Socratic dialogue - `extract_counterfactuals` - Generate what-if scenarios - `map_dependencies` - Chart logical dependencies ## Quick Start with Claude Desktop 1. **Install via uvx** (recommended): Edit your Claude Desktop config file: - **MacOS**: `~/Library/Application Support/Claude/claude_desktop_config.json` - **Windows**: `%APPDATA%\Claude\claude_desktop_config.json` Add this to the `mcpServers` section: ```json { "mcpServers": { "analysis-mcp": { "command": "uvx", "args": [ "git+https://github.com/YOUR_USERNAME/analysis_mcp", "analysis-mcp" ] } } } ``` 2. **Restart Claude Desktop** 3. **Verify installation**: Look for the 🔌 icon in Claude Desktop showing the analysis-mcp server is connected ## Alternative: Local Development Installation If you want to modify the code or run it locally: ```bash # Clone the repo git clone https://github.com/YOUR_USERNAME/analysis_mcp.git cd analysis_mcp # Create virtual environment python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate # Install in editable mode pip install -e ".[dev]" # Run tests pytest -v # Run server directly (for testing) python -m analysis_mcp.server ``` For local development in Claude Desktop, update your config to point to the local path: ```json { "mcpServers": { "analysis-mcp": { "command": "python", "args": [ "-m", "analysis_mcp.server" ], "cwd": "/absolute/path/to/analysis_mcp", "env": { "PYTHONPATH": "/absolute/path/to/analysis_mcp/src" } } } } ``` ## 🔄 Example Workflows ### Workflow 1: Deep Claim Analysis ``` 1. "Analyze: AI will replace all jobs in 10 years" → deconstruct_claim() → Get: assumptions, evidence, implications 2. "Now extract the assumptions from that analysis" → chain_analysis(prev, "extract_assumptions") → Get: implicit assumptions revealed 3. "Find contradictions in those assumptions" → chain_analysis(prev, "identify_contradictions") → Get: logical tensions 4. "Steelman the strongest version" → chain_analysis(prev, "steelman_argument") → Get: most defensible claim ``` ### Workflow 2: Multi-Lens Synthesis ``` 1. apply_lens("Fed raises rates", "economic") → Economic analysis 2. apply_lens("Fed raises rates", "political") → Political analysis 3. apply_operation(both_outputs, "synthesize_perspectives") → Unified framework 4. chain_analysis(synthesis, "identify_gaps") → Find what's missing ``` ### Workflow 3: Iterative Refinement ``` 1. compare_positions("Climate policy") → Multi-perspective view 2. chain_analysis(output, "elevate_abstraction") → Broader systemic patterns 3. chain_analysis(output, "ground_in_specifics") → Concrete examples added 4. chain_analysis(output, "check_coherence") → Verify consistency 5. chain_analysis(output, "suggest_next_step") → AI recommends next operation ``` ## 💡 Why This Approach? **Traditional Analysis:** One-shot, limited depth ``` "Analyze X" → Single output → Done ``` **Chained Analysis:** Iterative, building complexity ``` "Analyze X" → deconstruct → extract assumptions → find contradictions → steelman argument → identify gaps → synthesize = Deep, multi-layered understanding ``` **Benefits:** - ✅ **Build complexity incrementally** - Each operation adds a layer - ✅ **Provider-agnostic** - Works with any LLM - ✅ **No API keys needed** - Server never calls external LLMs - ✅ **Fully traceable** - Every step logged with trace_id - ✅ **Self-guided** - `suggest_next_step` operation recommends what to do next - ✅ **Composable** - Mix with other MCP tools (Wikipedia, web search, etc.) ## Available Lenses - **historical** - Compare to precedents and patterns - **economic** - Analyze resource flows and incentives - **geopolitical** - Examine power balances and strategy - **psychological** - Identify biases and manipulation - **technological** - Explore tech's role and impact - **sociocultural** - Analyze identity and narratives - **philosophical** - Apply ethical frameworks - **systems** - Map feedback loops and leverage points - **media** - Deconstruct framing and agenda-setting ## Trace Storage Analysis plans are logged to `~/.analysis_mcp/traces/` as JSON files. Each trace contains: - `trace_id` - Unique identifier - `tool` - Which tool was called - `input` - Original parameters - `outline` - Structured analysis plan - `next_prompt` - The prompt for LLM execution - `timestamp` - When it was created Use `get_trace(trace_id)` to retrieve any previous analysis plan. ## Troubleshooting **Server not connecting?** - Verify `uvx` is installed: `pip install uvx` - Check Claude Desktop logs (Help → View Logs) - Ensure your config JSON is valid **Tools not appearing?** - Restart Claude Desktop after config changes - Check the 🔌 icon shows "analysis-mcp" as connected ## Contributing Pull requests welcome! Please run tests before submitting: ```bash pytest -v ```