科学推理MCP服务器

1. Use Sequential Thinking for quick, linear reasoning
2. When uncertainty is high, escalate to PAS:
   → start_reasoning_session(user_goal="...")
   → Expand, critique, deepen until quality gate passes
3. Record outcome for RLVR learning

{
  "finalize_session": {
    "terminal_output": "[terminal logs here]",
    "auto_record": true
  }
}

git clone https://github.com/nocountryman/mcp-pas.git
cd mcp-pas

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Linux/Mac

# Install dependencies
pip install -r requirements.txt

# Generate LSIF index (optional)
pyright --outputtype lsif --outputfile project.lsif.json

# Import into PAS
# → import_lsif(project_id="...", lsif_path="/path/to/project.lsif.json")

# Create database
sudo -u postgres createdb mcp_pas

# Enable extensions
sudo -u postgres psql -d mcp_pas -c "CREATE EXTENSION IF NOT EXISTS vector;"
sudo -u postgres psql -d mcp_pas -c "CREATE EXTENSION IF NOT EXISTS ltree;"

# Run schema
psql -d mcp_pas -f schema.sql

# Seed scientific laws and domains
python seed_laws.py
python seed_domains_v2.py

cp env.template .env
# Edit .env with your database credentials

DATABASE_URL=postgresql://user:password@localhost:5432/mcp_pas

# Create separate venv with Python 3.12
python3.12 -m venv .venv312
source .venv312/bin/activate

# Install ROCm PyTorch from official wheels
pip install torch --index-url https://download.pytorch.org/whl/rocm6.2

# Install other dependencies
pip install -r requirements.txt

export HSA_OVERRIDE_GFX_VERSION=10.3.0

{
  "env": {
    "DATABASE_URL": "...",
    "HSA_OVERRIDE_GFX_VERSION": "10.3.0"
  }
}

source .venv312/bin/activate
HSA_OVERRIDE_GFX_VERSION=10.3.0 python -c "import torch; print(torch.cuda.is_available(), torch.cuda.get_device_name(0))"
# Expected: True AMD Radeon RX 6650 XT (or your GPU)

sudo pacman -S python-pytorch-cuda cuda cudnn

pip install torch --index-url https://download.pytorch.org/whl/cu121

{
  "mcpServers": {
    "pas-server": {
      "command": "/path/to/mcp-pas/.venv/bin/python",
      "args": ["/path/to/mcp-pas/server.py"],
      "env": {
        "DATABASE_URL": "postgresql://user:password@localhost:5432/mcp_pas",
        "HSA_OVERRIDE_GFX_VERSION": "10.3.0"
      }
    }
  }
}

1. Start session
   → start_reasoning_session(user_goal="Design a caching layer")

2. Expand hypotheses (3 alternatives)
   → prepare_expansion(session_id="...")
   → store_expansion(session_id="...", h1_text="...", h1_confidence=0.85, ...)

3. Critique top hypothesis
   → prepare_critique(node_id="...")  # Returns llm_prompt
   → [Process prompt]
   → store_critique(node_id="...", counterargument="...", severity_score=0.3)

4. Sequential gap analysis (REQUIRED before finalize)
   → prepare_sequential_analysis(session_id="...", top_n=3)
   → [Process 5-layer prompts]
   → store_sequential_analysis(session_id="...", results="[...]")

5. Deepen if score < 0.9 or gap < 0.1
   → store_expansion(parent_node_id="...", h1_text="Improved approach...", h1_confidence=0.95)

6. Get recommendation
   → finalize_session(session_id="...")
   # Returns: quality_gate, recommendation, exhaustive_check

1. Identify missing context
   → identify_gaps(session_id="...")

2. Ask questions one at a time
   → get_next_question(session_id="...")
   → [Present to user]
   → submit_answer(session_id="...", question_id="...", answer="B")

3. Check completion
   → check_interview_complete(session_id="...")
   # Once complete, hidden context is propagated to session

{
  "quality_gate": {
    "score": 0.95,
    "score_threshold": 0.9,
    "score_ok": true,
    "gap": 0.12,
    "gap_threshold": 0.1,
    "gap_ok": true,
    "passed": true
  }
}

{
  "score_improvement_suggestions": [
    {"lever": "score", "action": "Expand deeper with higher confidence (0.9+)"},
    {"lever": "gap", "action": "Explore more diverse alternatives"}
  ]
}

server.py                  Main MCP server (~5400 lines)
├── errors.py              Exception hierarchy (~200 lines)
├── utils.py               DB, embeddings, validation (~185 lines)
├── reasoning_helpers.py   Bayesian scoring, quality (~310 lines)
├── learning_helpers.py    RLVR, terminal parsing (~240 lines)
├── interview_helpers.py   Interview flow (~220 lines)
├── codebase_helpers.py    Symbol extraction (~260 lines)
├── sessions_helpers.py    Session lifecycle (~270 lines)
├── metacognitive_helpers.py  5-stage prompting (~200 lines)
├── preflight_helpers.py   Preflight enforcement (~180 lines)
├── calibration_helpers.py CSR calibration (~180 lines)
├── purpose_helpers.py     Hierarchical inference (~150 lines)
├── hybrid_helpers.py      Hypothesis synthesis (~120 lines)
└── self_awareness_helpers.py Schema introspection (~250 lines)

tests/
├── conftest.py            Fixtures + Layer 4 failure logging
├── test_tools/            Layer 1: Static domain tests
│   ├── test_reasoning.py  16 tests (9 tools)
│   ├── test_learning.py   6 tests (4 tools)
│   ├── test_codebase.py   8 tests (8 tools)
│   ├── test_metacognitive.py 7 tests (3 tools)
│   └── test_interview.py  5 tests (4 tools)
├── scenarios/             Layer 2: YAML workflow tests
├── test_scenarios.py      YAML runner
├── test_coverage.py       Layer 3: Self-aware coverage
└── test_server.py         Legacy v22/v23 tests

# Full suite with PAS failure logging
PAS_DB_NAME=mcp_pas pytest tests/ -v

# Fast mode (no failure logging)
PAS_DB_NAME=mcp_pas PAS_LOG_FAILURES=false pytest tests/ -v

# Run only unit tests
PAS_DB_NAME=mcp_pas pytest tests/test_tools/ -v

# Run YAML scenarios
PAS_DB_NAME=mcp_pas pytest tests/test_scenarios.py -v

# Check coverage (fails if new tool lacks tests)
PAS_DB_NAME=mcp_pas pytest tests/test_coverage.py -v

Missing tests for: [new_tool_name]