Dwizi + Claude: Deep Integration
Claude excels at reasoning and decision-making. Dwizi gives Claude the ability to act.
Together, they form a complete AI system.
How Claude Uses Dwizi Tools
When connected via MCP, Claude can:
- Discover available tools
- Understand tool inputs and outputs
- Decide when to invoke a tool
- Use the tool result in conversation
Claude treats Dwizi tools as first-class capabilities.
Typical Flow
- User asks Claude a question
- Claude determines a tool is needed
- Claude invokes the Dwizi tool
- Dwizi executes the tool in isolation
- Result is returned to Claude
- Claude responds with grounded output
Example: Data-Enriched Reasoning
User:
"Summarize today's sales performance."
Claude:
- Calls a Dwizi tool that queries internal data
- Receives structured results
- Generates a summary grounded in real data
Claude never touches infrastructure directly. Dwizi enforces execution boundaries.
Why This Matters
Claude remains:
- Focused on reasoning
- Free from infrastructure concerns
Dwizi ensures:
- Safe execution
- Predictable behavior
- Auditable access
This separation of concerns is critical for enterprise AI systems.
Best Practices
- Keep tools small and focused
- Use explicit schemas
- Avoid hidden state
- Prefer deterministic outputs
Claude performs best when tools are reliable and constrained.
Common Use Cases
- Internal data access
- Workflow automation
- File processing
- System integration
- Decision support
Summary
Claude reasons. Dwizi executes.
Together, they turn AI from conversation into action.