Mcp Change Analyzer

The Change Analyzer MCP Server addresses a common pain point for AI‑enabled development teams: quickly and reliably extracting actionable insights from the history of a Git repository. Traditional tooling for code metrics, change impact analysis, or directory health checks is often fragmented and requires manual orchestration. This server consolidates those capabilities into a single, A2A‑compatible endpoint that can be invoked by any Claude or other MCP‑aware assistant. By exposing a uniform API, it lets developers ask high‑level questions—such as “Which files changed most in the last sprint?” or “How many lines of code were added to each module?”—and receive structured, ready‑to‑consume data.

At its core, the server performs a deep scan of a Git repository supplied via a file path or remote URL. It walks the commit history, aggregates per‑commit statistics (added/removed lines, authorship, timestamps), and builds a directory tree view that reflects the current state of the codebase. The resulting metrics include commit counts, average churn per file, and heat‑maps of activity across modules. These insights are returned in JSON format, making them easy to feed into downstream AI reasoning or visualization pipelines. Because the server is built on the Agent‑to‑Agent protocol, it can participate in multi‑agent workflows: one assistant could request a change analysis, another could interpret the results to recommend refactoring, and yet another could generate documentation updates—all within a single conversation.

Key capabilities of the Change Analyzer include:

• Repository‑wide change metrics: Total lines added/removed, commit frequency, and contributor statistics.
• Directory structure analysis: Hierarchical view of files and folders with size, modification dates, and change density.
• Historical trend reporting: Graphs of activity over time that help identify hot spots or stale components.
• A2A integration: Exposes a simple RPC interface that any MCP client can call, enabling seamless embedding into existing AI toolchains.

Real‑world use cases span continuous integration pipelines that need to surface code churn before a merge, security teams that monitor for large, unreviewed changes, and product managers who want to gauge engineering effort on feature branches. For example, an AI assistant could automatically trigger the Change Analyzer when a pull request is opened, summarize the impact in natural language, and suggest code owners to review.

What sets this MCP server apart is its lightweight deployment model—requiring only Python 3.9+ and Redis for state—and its tight coupling with the A2A protocol, which guarantees that the server can act as both a data provider and an orchestrator in complex agent ecosystems. This makes it an ideal backbone for AI‑driven development workflows that demand real‑time, repository‑level intelligence.