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MCP for Coding Agents
External AI Coding Agents via MCP
Reqode can generate code with AI based on specifications. This works well for small and focused tasks.
But Reqode becomes even more powerful when you connect it to external AI coding agents using MCP (Model Context Protocol).
How It Works
Reqode provides dedicated MCP servers. These servers allow external AI coding agents to access your product specifications directly from Reqode.
You can keep using your favorite AI coding agent and connect it to Reqode through MCP. The agent will:
- Read structured specifications by modules
- Understand product architecture and guidelines
- Access software units tree (if defined in Reqode)
- Work within the correct product context
This creates a strong control layer over AI-generated code.
Why It Matters
When AI agents work without structured context, they often:
- Break architecture rules
- Miss product constraints
- Introduce inconsistencies
- Hallucinate missing logic
Reqode keeps the agent inside the real product boundaries. With Reqode as a context layer:
- The agent stays aligned with architecture
- Product constraints are visible
- Hallucinations are reduced
- Bug fixing becomes more predictable
- Refactoring becomes safer
Post-Development Validation
After the external coding agent completes development and creates commits, Reqode performs additional review. It checks each affected software unit for alignment with the relevant specifications, points out problems, and suggests specific corrections.
Read more about specs ↔ code alignment.
Architecture and Security Checks
Reqode can be configured to performs additional review of updated software units:
- Compliance with architectural guidelines
- Potential security risks
- Vulnerabilities in the code
Because Reqode has structured and up-to-date product context, security analysis becomes more effective. Traditional static code analysis works only with source code. Reqode analyzes code together with specifications, architecture rules, and product intent.
This allows:
- More accurate detection of risky changes
- Better understanding of intended behavior
- Reduced false positives
- Early identification of architectural violations