Digma’s MCP Server
The Digma MCP Server is powered by a Dynamic Code Analysis engine that enriches AI agents with deep runtime context. By analyzing APM data, it enables safe, performant, and context-aware code suggestions, code reviews, test generation, and issue resolution.
MCP Servers improve genAI suggestions
MCP servers allow AI models to access and interact with various tools and data sources, extending their capabilities beyond their own internal knowledge
Maximize code review effectiveness
Digma’s Dynamic Code Analysis engine provides the AI agent with insights based on observability data from production and pre-prod. The AI agent can then:
- Highlight risky code in the PR and provide impact visibility
- Automatically fix issues identified in pre-production
- Assess the proposed code based on real performance requirements
Optimize performance cut costs
Digma analyzes observability data to identify which issues to fix to improve performance and efficiency. The AI agent can then:
- Highlight the code areas that would have the most impact when optimized
- Automatically identify and fix performance bottlenecks, inefficient queries, scaling problems and other issues
Generating integration tests based on runtime data
Digma’s MCP server provides flexibility in creating integration tests, allowing developers to add use cases and leverage runtime data from test and production environments. The MCP server takes into account errors and exceptions from the tracing data while creating tests.
Analyze Performance Anomalies with Digma MCP
Digma’s Dynamic Code Analysis (DCA) engine and MCP server work together to optimize database queries and adjust Kafka send operations, creating a concrete plan for efficiently resolving issues. The DCA engine analyzes code-level observability data, identifying performance anomalies, while the MCP server processes this data, comparing traces, detecting systematic problems, and suggesting improvements.
