Reads local Git history and repository files to give coding agents structured status, structure, activity, and risk context.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.
One-click editor setup isn’t available for this listing yet — we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.
Inspect callable tools, capabilities, and parameters exposed to AI agents by Git Context MCP.
project_statusRepository state awareness
code_mapStructural understanding
recent_activityDevelopment churn analysis
work_summaryHigh-level progress overview
risk_scanMaintenance risk detection
The TamiShaks-2/git-context-mcp MCP server gives AI coding agents a structured view of a local Git repository. It focuses on context extraction rather than repository manipulation. The output is intended to help an agent determine the project’s current state, understand its layout, see which areas have been changing, and identify files that may deserve additional care.
Analysis covers both Git information and repository files. The server operates locally and exposes read-only tools, so it does not change tracked files, upload the codebase, or run the project’s code. It also does not rely on external services or APIs.
The server runs as a standalone local MCP process using STDIO transport. An MCP client connects to the process and invokes analysis tools against a local Git repository. The repository must be available on the machine where the server runs, and Git must be installed and available through the system PATH.
A practical analysis sequence starts with project_status to establish branch, cleanliness, and synchronization information. code_map can then provide a structural view of directories and important files. recent_activity shows development churn, while work_summary gives a broader progress and technical-debt view. risk_scan adds signals for files that may have elevated maintenance or bug risk.
The project requires Python 3.10 or newer and a local Git installation. The documented setup creates a virtual environment, activates it, and installs the repository in editable mode with pip install -e .. The server is then launched with the Python executable from that environment and src/server.py as its argument.
For MCP Inspector, configure the transport as STDIO. The repository documentation shows the virtual-environment Python executable as the command and src/server.py as the argument. No API keys, hosted endpoint, or service account is described. Tests can be run with pytest from the project environment.
The TamiShaks-2/git-context-mcp MCP server exposes five read-only tools:
project_status: Reports repository state, including branch, cleanliness, and synchronization details.code_map: Builds a structural directory view and identifies important files or entry points.recent_activity: Examines Git history to surface frequently modified or actively changing areas.work_summary: Produces a high-level view of recent work and open technical-debt indicators.risk_scan: Flags files associated with size, complexity, or high change frequency.Together, these tools provide high-level machine-readable context rather than raw source-code dumps. The TamiShaks-2/git-context-mcp MCP server is therefore suited to an initial repository assessment before code changes or review.
The server is local-first: it requires access to an existing Git repository and does not provide a hosted analysis service. Its documented scope is analysis, not editing, committing, synchronizing, or executing application code. The available material does not describe configurable environment variables, authentication, repository selection flags, or additional transports beyond STDIO.
Risk and activity results are analysis signals, not guarantees that a file contains a defect or should not be changed. Agents should use the results as context alongside the repository’s own code, tests, and review practices.
Factual signals from GitHub, npm, and our automated checks — not a rating.
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