CodeLogic MCP Server

Overview

The CodeLogic MCP server bridges the gap between an AI programming assistant and a rich, enterprise‑grade dependency graph. By exposing two targeted tools—codelogic-method-impact and codelogic-database-impact—the server lets the assistant query a CodeLogic deployment to understand how changes in one part of a codebase ripple through methods, classes, tables, columns, and views. For developers who routinely juggle large codebases with complex database schemas, this capability turns abstract “what‑if” questions into concrete, data‑driven answers.

The method impact tool retrieves an impact assessment for a specific method within its owning class. When an assistant suggests refactoring or adding logic, the tool can instantly reveal downstream callers, overridden implementations, and test coverage gaps. Similarly, the database impact tool maps a database entity to its usage in code—identifying which queries, ORM models, or stored procedures reference a column, table, or view. This reverse‑engineering insight is invaluable when preparing for schema migrations, performance tuning, or compliance audits.

Key capabilities include:

• Real‑time dependency analysis: The server queries the CodeLogic API on demand, ensuring that the assistant works with the latest metadata without manual refreshes.
• Fine‑grained context: By passing both a method and its class, or a database entity type and name, the tools return precise impact sets tailored to the current code snippet.
• Seamless IDE integration: Configurable via for VS Code, Cursor, or Claude Desktop, the server plugs directly into agent mode workflows, allowing developers to toggle tools in the chat UI and receive instant results.
• Cross‑platform operability: The server runs on any platform supported by Astral UV, with a documented MacOS workaround for issues that commonly affect IDEs like Cursor.

Typical use cases include:

• Refactoring safety checks: Before moving a method, the assistant can list all callers and related database accesses to ensure no unintended side effects.
• Database migration planning: When a table or column is slated for deprecation, the assistant can surface all code references and suggest alternative patterns.
• Compliance and audit support: By mapping data flow from source code to database entities, teams can demonstrate lineage for regulatory reviews.
• Rapid onboarding: New developers can ask the assistant to explain the impact of a feature, accelerating their understanding of legacy systems.

By integrating CodeLogic’s dependency data directly into AI workflows, this MCP server transforms passive code suggestions into informed, context‑aware decisions. Developers gain a powerful ally that can answer “What happens if I change this?” in seconds, reducing risk and speeding delivery across complex software ecosystems.