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 componentscompare_positions- Multi-perspective ideological analysisapply_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 contentchain_analysis- Chain operations on previous LLM outputslist_available_operations- Browse all available operations
15+ Analytical Operations:
Deconstructive:
extract_assumptions- Find implicit/explicit assumptionsidentify_contradictions- Spot logical tensionsfind_fallacies- Detect rhetorical manipulation
Constructive:
steelman_argument- Build strongest versionfind_analogies- Identify relevant precedentsextract_principles- Derive universal patterns
Synthetic:
synthesize_perspectives- Merge viewpointselevate_abstraction- Raise to higher conceptsground_in_specifics- Add concrete examples
Meta-analytical:
identify_gaps- Find missing elementscheck_coherence- Verify logical consistencysuggest_next_step- Recommend next operation
Transformative:
convert_to_dialogue- Reframe as Socratic dialogueextract_counterfactuals- Generate what-if scenariosmap_dependencies- Chart logical dependencies
Quick Start with Claude Desktop
- 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:
{
"mcpServers": {
"analysis-mcp": {
"command": "uvx",
"args": [
"git+https://github.com/YOUR_USERNAME/analysis_mcp",
"analysis-mcp"
]
}
}
}
-
Restart Claude Desktop
-
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:
# 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:
{
"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_stepoperation 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 identifiertool- Which tool was calledinput- Original parametersoutline- Structured analysis plannext_prompt- The prompt for LLM executiontimestamp- When it was created
Use get_trace(trace_id) to retrieve any previous analysis plan.
Troubleshooting
Server not connecting?
- Verify
uvxis 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:
pytest -v