# think-mcp Comprehensive Evaluation Summary
**Generated:** 2025-12-28
**Evaluation Type:** Semantic Analysis & Tool Calling Accuracy
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## Task 1: Semantic Evaluation Results
### Overview
- **Total Tests Evaluated:** 47
- **Tools Covered:** All 11 think-mcp v2.0 tools
- **Evaluation Dimensions:**
- Coherence Score (0.0-1.0)
- Usefulness Score (0.0-1.0)
- Factual Equivalence (equivalent/superset/subset/disagreement)
- Judge Reasoning (qualitative analysis)
### Aggregate Semantic Scores
#### By Tool
| Tool | Avg Coherence | Avg Usefulness | Tests |
|------|--------------|----------------|-------|
| trace | 0.86 | 0.78 | 5 |
| model | 0.93 | 0.89 | 6 |
| pattern | 0.90 | 0.88 | 6 |
| paradigm | 0.90 | 0.88 | 6 |
| debug | 0.88 | 0.88 | 6 |
| council | 0.88 | 0.88 | 2 |
| decide | 0.95 | 0.90 | 3 |
| reflect | 0.92 | 0.88 | 3 |
| hypothesis | 0.92 | 0.90 | 3 |
| debate | 0.93 | 0.90 | 4 |
| map | 0.90 | 0.87 | 3 |
**Overall Average Coherence:** 0.90
**Overall Average Usefulness:** 0.87
#### Top Performing Tools (Coherence)
1. **decide** (0.95) - Excellent structured decision frameworks
2. **model** (0.93) - Strong mental model application
3. **debate** (0.93) - Well-structured dialectical reasoning
#### Top Performing Tools (Usefulness)
1. **model** (0.89) - Highly actionable mental models
2. **decide** (0.90) - Very practical decision guidance
3. **hypothesis** (0.90) - Effective scientific method application
### Key Findings
#### Strengths
1. **Revision & Branching (trace):** Edge tests showed excellent support for thought revision (0.9 coherence) and branching alternatives (0.95 coherence)
2. **Model Selection:** Consistently appropriate mental model matching (opportunity_cost for build/buy, pareto for prioritization)
3. **Dialectical Reasoning (debate):** Perfect thesis-antithesis-synthesis progression (0.95 coherence for synthesis)
4. **Error Handling:** Proper validation with clear error messages (1.0 coherence scores)
5. **Multi-stage Tools:** Council, decide, hypothesis, and map all demonstrate coherent stage progression
#### Areas for Enhancement
1. **trace Edge Case:** Lenient validation accepting negative thoughtNumber (0.6 coherence) - should enforce positive integers
2. **Code Examples:** Pattern tool would benefit from more code examples (present in only 1/6 tests)
3. **Content Depth:** Some tools echo inputs without elaboration - actual reasoning steps would boost usefulness
### Factual Equivalence Distribution
- **Equivalent:** 41 tests (87%)
- **Superset:** 6 tests (13%) - tools provided additional valuable context
- **Subset:** 0 tests (0%)
- **Disagreement:** 0 tests (0%)
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## Task 2: Tool Calling Accuracy Results
### Overview
- **Total Scenarios:** 33
- **Categories:** 9 (sequential-reasoning, mental-models, design-patterns, programming-paradigms, debugging, collaborative-reasoning, decision-making, metacognition, scientific-method, argumentation, visual-reasoning)
### Accuracy Metrics
| Metric | Count | Percentage |
|--------|-------|------------|
| **Correct Tool Called** | 31/33 | 93.9% |
| **Acceptable Tool Called** | 33/33 | 100.0% |
| **Perfect Match (only 1 acceptable)** | 24/33 | 72.7% |
### Tool Selection Accuracy by Category
| Category | Scenarios | Correct | Acceptable | Accuracy |
|----------|-----------|---------|------------|----------|
| sequential-reasoning | 3 | 2 | 3 | 66.7% / 100% |
| mental-models | 3 | 3 | 3 | 100% / 100% |
| design-patterns | 3 | 3 | 3 | 100% / 100% |
| programming-paradigms | 3 | 3 | 3 | 100% / 100% |
| debugging | 3 | 3 | 3 | 100% / 100% |
| collaborative-reasoning | 3 | 3 | 3 | 100% / 100% |
| decision-making | 3 | 3 | 3 | 100% / 100% |
| metacognition | 3 | 3 | 3 | 100% / 100% |
| scientific-method | 3 | 3 | 3 | 100% / 100% |
| argumentation | 3 | 3 | 3 | 100% / 100% |
| visual-reasoning | 3 | 3 | 3 | 100% / 100% |
### Decision Analysis for Non-Perfect Matches
#### accuracy-trace-002
- **Question:** "Let me think through this problem carefully. I need to decide whether to use a monolith or microservices"
- **Expected:** trace
- **Called:** decide
- **Status:** Acceptable but not correct
- **Reasoning:** While "think through" suggests trace, the core task is decision-making between two architectural options. The decide tool is more appropriate for structuring decision criteria and providing a recommendation.
#### accuracy-trace-001
- **Question:** "Walk me through solving this step by step - I need to figure out why our database queries are slow"
- **Called:** trace
- **Alternative:** debug could also be appropriate
- **Reasoning:** "Step by step" and "walk me through" strongly indicate trace for sequential reasoning, but the underlying problem (slow queries) is a debugging scenario where debug tool would also be valid.
### Tool Selection Patterns
#### Clear Indicators
1. **trace:** "step by step", "one thought at a time", "walk me through"
2. **model:** "apply [model name]", "what mental model", "first principles"
3. **pattern:** "what design pattern", "pattern for", "how should I architect"
4. **paradigm:** "OOP or functional", "programming approach", "reactive programming"
5. **debug:** "debug", "root cause", "track it down", "why is failing"
6. **council:** "multiple perspectives", "different experts", "diverse viewpoints"
7. **decide:** "choose between", "help me decide", "structured decision"
8. **reflect:** "am I thinking correctly", "what am I missing", "knowledge gaps"
9. **hypothesis:** "test my assumption", "validate my theory", "design an experiment"
10. **debate:** "arguments for and against", "challenge my position", "both sides"
11. **map:** "visualize", "draw out", "diagram"
#### Ambiguous Cases
- Decision scenarios can use either **decide** (structured framework) or **council** (multiple perspectives)
- Debugging can use **debug** (systematic approach) or **hypothesis** (testing specific theories)
- "Think through" can mean **trace** (sequential) or the appropriate domain tool
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## Overall Assessment
### Think-mcp v2.0 Quality
- **High Coherence (0.90):** Tools demonstrate logical reasoning flow
- **High Usefulness (0.87):** Outputs are actionable and helpful
- **Excellent Tool Diversity:** 11 distinct tools cover comprehensive reasoning patterns
- **Strong Validation:** Error handling maintains data integrity
### Tool Selection Intelligence Required
- **93.9% accuracy** suggests most scenarios have clear tool mappings
- **100% acceptable accuracy** shows the acceptable tool lists are well-calibrated
- **Language cues** (e.g., "visualize" → map, "multiple perspectives" → council) are strong indicators
### Recommendations
#### For Tool Developers
1. Consider enforcing positive integers for thoughtNumber in trace
2. Increase code example coverage in pattern tool
3. Ensure tools provide elaborated reasoning, not just input echoes
#### For Tool Users
1. Use explicit keywords to signal tool intent ("visualize" for map, "apply X model" for model)
2. For ambiguous scenarios, consider which dimension is primary:
- Decision structure → decide
- Multiple viewpoints → council
- Systematic debugging → debug
- Theory testing → hypothesis
#### For LLM Integration
1. Train on language cues mapping to specific tools
2. Recognize compound scenarios (e.g., "think through a decision" could use trace → decide pipeline)
3. Default to domain-specific tools when ambiguous (debug for bugs, decide for choices)
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## Files Generated
1. **Semantic Evaluation:**
`/Users/kesh/Documents/Github -Local/Mental Models MCPs/think-mcp/test-results/think-mcp-eval-results-semantic.jsonl`
47 test results with coherence scores, usefulness scores, factual equivalence, and judge reasoning
2. **Tool Accuracy:**
`/Users/kesh/Documents/Github -Local/Mental Models MCPs/think-mcp/test-results/tool-accuracy-results.jsonl`
33 scenarios with expected vs actual tool calls and detailed reasoning
3. **Summary Report:**
`/Users/kesh/Documents/Github -Local/Mental Models MCPs/think-mcp/test-results/comprehensive-evaluation-summary.md`
This document
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## Conclusion
The think-mcp v2.0 toolset demonstrates **strong semantic quality** (90% coherence, 87% usefulness) and **excellent tool selection clarity** (94% accuracy). The tools are well-differentiated, with clear language cues enabling accurate tool selection. Minor improvements in validation strictness and content depth would further enhance the framework's utility for complex reasoning tasks.
