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CodeView MCP Server

@mann-uofg

RepositoryHomepage

Basic information

Category

Developer Tools

License

MIT

Transports

stdio

Author

mann-uofg

About CodeView MCP Server

AI-powered code-review toolkit: MCP server + CLI to analyze GitHub PRs with local LLM smells, cloud LLM summaries, inline comments, risk gating, and test stub generation.

#codeview#code-review#ai-toolkit

CodeView MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "codeview-mcp": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        ".venv",
        "&&",
        "source",
        ".venv/bin/activate"
      ]
    }
  }
}

CodeView MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is CodeView MCP 🪄?

CodeView MCP 🪄 is an MCP server that performs a 30-second AI review of pull requests, combining static regex rules for critical smells, a local LLM (CodeLlama-13B) for quick heuristics, and a cloud LLM (Llama-3.1-8b-instant via Groq/OpenAI) for human-style summaries and risk scores. It targets developers who need fast, privacy-conscious PR analysis.

How to use CodeView MCP 🪄?

Clone the repository, create a Python virtual environment, install dependencies, and install the package with pip install -e .. Run a smoke test with reviewgenie/codeview ping https://github.com/psf/requests/pull/6883. Store secrets such as a GitHub PAT and Groq/OpenAI API key using the codeview_mcp.secret keyring module.

Key features of CodeView MCP 🪄

  • Static regex rules for critical code smells
  • Local LLM heuristics with no cloud cost
  • Cloud LLM human-style summary and risk score (0–1)
  • One-click inline comment accept or ignore
  • SQLite diff cache and ChromaDB hunk embeddings
  • OpenTelemetry tracing and GitHub back-off logic

Use cases of CodeView MCP 🪄

  • Reviewing large PRs quickly for security or performance issues
  • Running a CI gate that blocks merges when risk score exceeds a threshold
  • Generating stub test files and opening test PRs automatically
  • Auditing code with inline comments that can be accepted or dismissed

Frequently asked questions

1

What does the `analyze` tool produce?

It returns a summary, a list of smells, rule hits, and a risk score between 0 and 1, with typical latency of 6–10 seconds.

2

How does CodeView MCP 🪄 protect code privacy?

Only the diff snippet is sent to the cloud LLM (Groq); the full codebase never leaves your machine.

3

What LLMs does CodeView MCP 🪄 use?

It uses CodeLlama-13B locally and Llama-3.1-8b-instant via a cloud provider (Groq or OpenAI, configured via `OPENAI_API_KEY` and `OPENAI_BASE_URL`).

4

What are the runtime dependencies?

Python 3.10+, SQLite, ChromaDB, and network access for cloud LLM calls. Secrets are stored via the system keyring.

5

How is the server invoked or transported?

The README shows a CLI command (`reviewgenie/codeview`) and smoke test; the MCP transport is not specified further.

Comments

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