MCP-Logic
by angrysky56
- mcp-logic
- .handoff_docs
# IntegrationGuides
## Purpose and Overview
The MCP Logic server is designed to integrate with various AI and knowledge management systems. This guide covers integration patterns, best practices, and common use cases for:
1. AI System Integration:
- Formal verification of AI reasoning
- Knowledge consistency checking
- Automated theorem proving support
- Logical inference capabilities
2. Knowledge Base Integration:
- Theorem storage and retrieval
- Proof history tracking
- Dependency management
- Pattern recognition
3. Development Integration:
- API usage patterns
- Error handling
- Resource management
- Performance optimization
This guide provides concrete examples and implementation patterns for each integration scenario.
[Why this domain is critical to the project]
## Step-by-Step Explanations
### AI System Integration
1. Setting Up MCP Client:
```python
from mcp.client import Client
async def setup_logic_client():
client = Client()
await client.connect("mcp-logic")
return client
```
2. Implementing Verification:
```python
class AIVerifier:
def __init__(self, mcp_client: Client):
self.client = mcp_client
async def verify_reasoning(
self,
premises: list[str],
conclusion: str
) -> bool:
response = await self.client.call_tool(
"prove",
{
"premises": premises,
"conclusion": conclusion
}
)
return response[0].success
```
3. Knowledge Consistency:
```python
async def check_consistency(knowledge_base: list[str]) -> bool:
# Check that knowledge base doesn't imply a contradiction
result = await prove(
premises=knowledge_base,
conclusion="P & -P"
)
return not result.success
```
### Knowledge Base Integration
1. Neo4j Setup:
```python
from neo4j import GraphDatabase
class TheoremStore:
def __init__(self, uri: str):
self.driver = GraphDatabase.driver(uri)
async def store_theorem(self, theorem: Theorem):
await self.driver.execute_query("""
MERGE (t:Theorem {
name: $name,
premises: $premises,
conclusion: $conclusion
})
""", theorem.__dict__)
```
2. Proof History:
```python
async def track_proof_history(proof: ProofResult):
await neo4j.execute_query("""
MATCH (t:Theorem {name: $name})
CREATE (p:Proof {
timestamp: datetime(),
steps: $steps,
success: $success
})
CREATE (t)-[:HAS_PROOF]->(p)
""", {
"name": proof.theorem_name,
"steps": proof.steps,
"success": proof.success
})
```
3. Pattern Recognition:
```python
async def find_similar_theorems(theorem: Theorem):
return await neo4j.execute_query("""
MATCH (t:Theorem)
WHERE any(p IN t.premises WHERE p IN $premises)
AND t.conclusion = $conclusion
RETURN t
""", {
"premises": theorem.premises,
"conclusion": theorem.conclusion
})
```
### Development Integration
1. Error Handling:
```python
class LogicIntegration:
async def safe_prove(
self,
premises: list[str],
conclusion: str
) -> ProofResult:
try:
return await self.client.call_tool(
"prove",
{
"premises": premises,
"conclusion": conclusion
}
)
except Exception as e:
logger.error(f"Proof error: {e}")
return ProofResult(
success=False,
error=str(e)
)
```
2. Resource Management:
```python
class ProofManager:
def __init__(self, max_concurrent: int = 4):
self.semaphore = asyncio.Semaphore(max_concurrent)
self.active_proofs = set()
async def prove(self, *args, **kwargs):
async with self.semaphore:
proof_id = str(uuid.uuid4())
self.active_proofs.add(proof_id)
try:
return await self.client.prove(*args, **kwargs)
finally:
self.active_proofs.remove(proof_id)
```
3. Performance Monitoring:
```python
class PerformanceMonitor:
def __init__(self):
self.metrics = defaultdict(list)
async def track_operation(
'prove',
self.prover.prove(
premises,
conclusion,
timeout=t
)
)
if result.success:
# Cache successful proof
self.cache[cache_key] = result
return result
elif result.has_counterexample:
# Cache counterexample
self.cache[cache_key] = result
return result
return ProofResult(
success=False,
reason="timeout_exhausted"
)
def make_cache_key(
self,
premises: List[str],
conclusion: str
) -> str:
# Create stable cache key
sorted_premises = sorted(premises)
return f"{','.join(sorted_premises)}|{conclusion}"
```
### 4. Modal Logic Integration Example
```python
class ModalLogicVerifier:
def __init__(self, mcp_client: Client):
self.client = mcp_client
self.known_operators = {
'box': 'necessity',
'dia': 'possibility',
'knows': 'knowledge'
}
async def verify_modal_statement(
self,
modal_premises: List[str],
modal_conclusion: str,
logic_type: str = 'S4' # Default to S4 modal logic
) -> ProofResult:
# Add modal logic axioms based on type
axioms = self.get_modal_axioms(logic_type)
all_premises = axioms + modal_premises
return await self.client.call_tool(
"prove",
{
"premises": all_premises,
"conclusion": modal_conclusion,
"logic_type": "modal"
}
)
def get_modal_axioms(self, logic_type: str) -> List[str]:
# Basic modal logic axioms
axioms = [
"all x (box(x) -> -dia(-x))", # Duality of box/diamond
"all x y (box(x -> y) -> (box(x) -> box(y)))" # K axiom
]
if logic_type == 'S4':
# Add S4 specific axioms
axioms.extend([
"all x (box(x) -> x)", # T axiom
"all x (box(x) -> box(box(x)))" # 4 axiom
])
elif logic_type == 'S5':
# Add S5 specific axioms
axioms.extend([
"all x (box(x) -> x)", # T axiom
"all x (-box(x) -> box(-box(x)))" # 5 axiom
])
return axioms
```
## Contextual Notes
### Integration Design Patterns
1. Service Layer Pattern:
- Separate MCP interface from business logic
- Handle connection lifecycle
- Manage resources efficiently
- Provide clean error handling
2. Repository Pattern:
- Abstract theorem storage
- Handle data persistence
- Manage proof history
- Enable pattern analysis
3. Event-Driven Integration:
- React to proof completion
- Track system metrics
- Handle timeouts gracefully
- Maintain audit logs
### Performance Considerations
1. Caching Strategy:
- Cache proven theorems
- Store counterexamples
- Index common patterns
- Manage cache invalidation
2. Resource Management:
- Control concurrent proofs
- Monitor memory usage
- Handle long-running proofs
- Clean up resources
3. Error Recovery:
- Graceful degradation
- Retry mechanisms
- Circuit breakers
- Fallback strategies
## Actionable Advice
### Best Practices
1. Connection Management:
- Use connection pools
- Implement timeouts
- Handle reconnection
- Clean up resources
2. Error Handling:
- Define error types
- Provide context
- Log appropriately
- Recover gracefully
3. Testing:
- Unit test integration points
- Simulate failures
- Test timeout handling
- Verify resource cleanup
### Common Pitfalls
1. Integration Issues:
- Unhandled connection errors
- Resource leaks
- Missing timeouts
- Incorrect error handling
2. Performance Problems:
- Unbounded caches
- Resource exhaustion
- Blocking operations
- Memory leaks
3. Maintenance Challenges:
- Poor monitoring
- Inadequate logging
- Missing documentation
- Unclear error messages
### Troubleshooting Guide
1. Connection Issues:
```python
async def diagnose_connection():
try:
# Check MCP connection
await client.ping()
# Verify Prover9 access
await prover.test_connection()
# Test database access
await storage.ping()
except Exception as e:
logger.error(f"Connection error: {e}")
return False
return True
```
2. Performance Issues:
```python
async def check_performance():
metrics = await collect_metrics()
# Check proof times
if metrics.avg_proof_time > 5.0:
logger.warning("High average proof time")
# Monitor memory
if metrics.memory_usage > 80:
logger.warning("High memory usage")
# Track success rate
if metrics.proof_success_rate < 0.9:
logger.warning("Low proof success rate")
```
3. Resource Monitoring:
```python
class ResourceMonitor:
def __init__(self):
self.active_connections = set()
self.proof_attempts = 0
self.memory_usage = []
async def monitor(self):
while True:
stats = {
"connections": len(self.active_connections),
"proofs": self.proof_attempts,
"memory": sum(self.memory_usage) / len(self.memory_usage)
}
await log_metrics(stats)
await asyncio.sleep(60)
```
self,
operation_name: str,
coroutine: Awaitable[T]
) -> T:
start_time = time.time()
try:
result = await coroutine
duration = time.time() - start_time
self.metrics[operation_name].append({
"duration": duration,
"success": True
})
return result
except Exception as e:
duration = time.time() - start_time
self.metrics[operation_name].append({
"duration": duration,
"success": False,
"error": str(e)
})
raise
```
[Concrete, detailed steps for implementation and maintenance]
## Annotated Examples
[Code snippets, diagrams, or flowcharts for clarity]
## Contextual Notes
[Historical decisions, trade-offs, and anticipated challenges]
## Actionable Advice
[Gotchas, edge cases, and common pitfalls to avoid]