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CodeRabbit

AI code reviews with MCP-connected context

CodeRabbit reviews pull requests, supports IDE and CLI workflows, and can pull in context from connected MCP servers. It’s aimed at teams that want more context-aware review comments and code suggestions without changing their existing review process.

iOS
Slack
Integrations
B2B
CLI
IDE Extension
MCP Support
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About

What It Is

CodeRabbit is an AI code review tool for software teams. Based on the documentation, it works across pull requests, an IDE review extension, a command-line review tool, and a Slack agent, so it fits into common development workflows rather than replacing them.

Its MCP integration lets CodeRabbit act as an MCP client and connect to external tools and data sources such as documentation systems, project management tools, knowledge bases, and design tools. The goal is to give reviews and PR chat more context from your existing systems so suggestions can be better grounded in project-specific information.

Setup starts in the CodeRabbit app’s Integrations page, where you add a new MCP server, complete authentication, and choose which MCP tools to enable. The docs also recommend adding free-text user guidance so CodeRabbit knows what the server contains and what information matters for code reviews.

What to Know

This is a practical integration layer for teams already using CodeRabbit, not a general-purpose agent platform. It can automatically call relevant MCP tools during reviews and chat, but the documentation frames this as context enrichment for review workflows rather than fully autonomous software delivery.

A few limits are clear from the docs: CodeRabbit is the MCP client, not the server, and the number of MCP connections depends on plan level. Pro supports up to 5 MCP servers, Pro+ up to 15, and Enterprise up to 20. Pricing itself is not publicly listed on the page, and the underlying AI model providers are not specified here. If you are looking for a local, self-hosted, or model-tunable review system, this page does not show that capability.

Key Features
Reviews pull requests with AI-assisted analysis
Connects to external tools through MCP servers
Uses connected data sources during code review and PR chat
Enriches review comments with external context
Lets you enable or disable individual MCP tools per server
Use Cases
A team wants PR reviews to reference internal documentation, architecture notes, or API specs
A reviewer wants AI comments to be validated against project management or knowledge-base context
An engineering org wants to connect design references such as Figma to code review workflows
Agenticness: Guided Assistant

Executes tasks you assign, one step at a time, within narrow domains.

High evidence
Last evaluated: Apr 23, 2026

Dimension Breakdown

Action Capability
Autonomy
Adaptation
State & Memory
Safety

Categories

Pricing
  • Pro: Supports up to 5 MCP server connections.
  • Pro+: Supports up to 15 MCP server connections.
  • Enterprise: Supports up to 20 MCP server connections.
  • Pricing: Public pricing not available on the provided documentation page.
Details
AddedApril 23, 2026
RefreshedApril 23, 2026
Agenticness
Quick Facts
DeploymentCloud-hosted
AutonomySemi-autonomous
Model supportSingle model
Open sourceNo
MCP supportYes
Team supportEnterprise
Pricing modelSubscription
Interfacechat, gui, cli, ide, api
Sources
Last updated April 23, 2026
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