Implement Patent Analyst Agent with Qdrant

Using the Claude Agents SDK, implement the 'Patent Analyst Agent'. This agent should be capable of: 
1. Receiving a user query about patent novelty or prior art. 
2. Utilizing Claude Opus 4.1 for deep textual understanding of patent claims. 
3. Interacting with a Qdrant vector database (via a custom tool) to search for relevant prior art documents based on the claim text's embeddings. 
4. Synthesizing an assessment of the patent's novelty, referencing retrieved documents. 

Ensure your agent defines tools for interacting with Qdrant and uses Claude's advanced reasoning. You can mock the Qdrant interaction if a full setup is too complex initially.

```python
# Example of Claude Agents SDK basic structure (simplified)
# This will vary based on the latest SDK version, focus on patterns.

from anthropic.agents import AnthropicAgent, Tool

def search_qdrant(query: str) -> str:
    # Simulate Qdrant search
    print(f"Searching Qdrant for: {query}")
    return "Found relevant prior art document US9876543B1 related to AI content generation."

qdrant_tool = Tool(
    name="qdrant_search",
    description="Searches the Qdrant vector database for patent documents.",
    input_schema={"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]},
    function=search_qdrant
)

# Your agent definition will incorporate this tool
# agent = AnthropicAgent(model="claude-opus-4.1", tools=[qdrant_tool], ...)
```