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Code Review MCP Server

English | δΈ­ζ–‡

PyPI version License: MIT Python 3.10+

MCP (Model Context Protocol) server for code review. Enables AI assistants to review GitHub/GitLab Pull Requests and Merge Requests.

Keywords: MCP server, AI code review, GitHub pull request review, GitLab merge request, Cursor IDE, Claude Desktop, Model Context Protocol, automated PR comments.

✨ Features

  • πŸ” Multi-platform: Supports both GitHub and GitLab (including self-hosted)

  • πŸš€ Multiple Transports: Supports stdio, SSE, and WebSocket protocols

  • πŸ“¦ Easy Install: Quick install via uvx or pip

  • 🐳 Containerized: Docker image available

  • ☁️ Cloud Deploy: One-click Smithery deployment

  • πŸ”’ Security First: Environment variable configuration, no data persistence

Related MCP server: GitHub Code Review Assistant

πŸš€ Quick Start

# Run directly, no installation needed
uvx code-review-mcp

Option 2: Using pip

pip install code-review-mcp

# Run the server
code-review-mcp

# (Optional) Install Cursor rules to your project
code-review-mcp init-rules

Option 3: From Source

git clone https://github.com/OldJii/code-review-mcp.git
cd code-review-mcp
pip install -e .
code-review-mcp

πŸ”§ Configuration

Environment Variables

Variable

Description

Required

GITHUB_TOKEN

GitHub personal access token

When using GitHub

GITLAB_TOKEN

GitLab personal access token

When using GitLab

GITLAB_HOST

GitLab host URL

For self-hosted (default: gitlab.com)

Getting Tokens

GitHub

# Option 1: Using gh CLI (Recommended)
brew install gh
gh auth login

# Option 2: Manual Token Creation
# Visit https://github.com/settings/tokens
# Create Personal Access Token with 'repo' scope
export GITHUB_TOKEN="your-token-here"

GitLab

# Option 1: Using glab CLI (Recommended)
brew install glab
glab auth login

# For self-hosted GitLab
glab auth login --hostname gitlab.yourcompany.com

# Option 2: Manual Token Creation
# Visit GitLab -> Settings -> Access Tokens
# Create token with 'api' scope
export GITLAB_TOKEN="your-token-here"
export GITLAB_HOST="gitlab.yourcompany.com"  # For self-hosted

πŸ“± Client Configuration

Cursor

Edit ~/.cursor/mcp.json:

{
  "mcpServers": {
    "code-review": {
      "command": "uvx",
      "args": ["code-review-mcp"],
      "env": {
        "GITHUB_TOKEN": "your-github-token",
        "GITLAB_TOKEN": "your-gitlab-token"
      }
    }
  }
}

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "code-review": {
      "command": "uvx",
      "args": ["code-review-mcp"],
      "env": {
        "GITHUB_TOKEN": "your-github-token",
        "GITLAB_TOKEN": "your-gitlab-token"
      }
    }
  }
}

SSE Mode (Remote Deployment)

# Start SSE server
code-review-mcp --transport sse --port 8000

Client configuration:

{
  "mcpServers": {
    "code-review": {
      "url": "http://your-server:8000/sse"
    }
  }
}

WebSocket Mode (Remote Deployment)

# Start WebSocket server
code-review-mcp --transport websocket --port 8000

Client configuration:

{
  "mcpServers": {
    "code-review": {
      "url": "ws://your-server:8000/ws"
    }
  }
}

🐳 Docker Deployment

Build Image

docker build -t code-review-mcp .

Run Container

stdio mode

docker run -i --rm \
  -e GITHUB_TOKEN="your-token" \
  code-review-mcp

SSE mode

docker run -d --rm \
  -e GITHUB_TOKEN="your-token" \
  -p 8000:8000 \
  code-review-mcp --transport sse

πŸ”¨ MCP Tools

Rules

Tool

Description

get_review_rules

Get review rules (builtin + custom project rules)

Information Retrieval

Tool

Description

get_pr_info

Get PR/MR details (title, description, branches)

get_pr_changes

Get code changes (diff), supports file type filtering

extract_related_prs

Extract related PR/MR links from description

Adding Comments

Tool

Description

add_inline_comment

Add inline comment to specific code line

add_pr_comment

Add general comment

batch_add_comments

Batch add comments (inline + general)

πŸ’¬ Usage Examples

Chat with Cursor or Claude:

Review GitHub PR

Review https://github.com/facebook/react/pull/12345

Review GitLab MR

Review https://gitlab.com/group/project/-/merge_requests/678

Review Self-hosted GitLab MR

Review https://gitlab.yourcompany.com/team/project/-/merge_requests/90

Review Only Specific File Types

Review this PR, only check .py and .js files:
https://github.com/owner/repo/pull/123

πŸ§ͺ Debugging & Testing

Using MCP Inspector

# Run with MCP Inspector
npx @modelcontextprotocol/inspector uvx code-review-mcp

This launches a web interface where you can:

  • View all available tools

  • Manually call tools and inspect results

  • Debug parameters and responses

Local Development

# Clone repository
git clone https://github.com/OldJii/code-review-mcp.git
cd code-review-mcp

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Debug with Inspector
npx @modelcontextprotocol/inspector python -m code_review_mcp.server

πŸ“ Project Structure

code-review-mcp/
β”œβ”€β”€ src/
β”‚   └── code_review_mcp/
β”‚       β”œβ”€β”€ __init__.py      # Package entry
β”‚       β”œβ”€β”€ cli.py           # CLI commands (init-rules, etc.)
β”‚       β”œβ”€β”€ server.py        # MCP server main logic
β”‚       β”œβ”€β”€ providers.py     # GitHub/GitLab providers
β”‚       └── rules/           # Bundled Cursor rules
β”‚           β”œβ”€β”€ code-review.mdc
β”‚           └── code-review-en.mdc
β”œβ”€β”€ pyproject.toml           # Project config & PyPI publishing
β”œβ”€β”€ Dockerfile               # Docker build file
β”œβ”€β”€ smithery.yaml            # Smithery deployment config
β”œβ”€β”€ CHANGELOG.md             # Changelog
β”œβ”€β”€ CONTRIBUTING.md          # Contributing guide
└── README.md                # Documentation

This package includes built-in code review rules for Cursor IDE. Install them to your project with one command:

# Install rules to current project
code-review-mcp init-rules

# Install to a specific directory
code-review-mcp init-rules --target /path/to/project

# Overwrite existing rules
code-review-mcp init-rules --force

# List available rules
code-review-mcp list-rules

After installation, the rules will be available in your project's .cursor/rules/ directory:

  • code-review.mdc - Chinese version

  • code-review-en.mdc - English version

Custom Project Rules

You can define project-specific review rules that the MCP server loads at runtime. This allows each project to enforce its own coding standards during reviews.

Quick Setup:

# Generate a custom rules template
code-review-mcp init-rules --custom

This creates .code-review-rules/project-rules.md in your project. Edit it with your project-specific conventions, then configure the MCP server to load it:

{
  "mcpServers": {
    "code-review": {
      "command": "uvx",
      "args": ["code-review-mcp"],
      "env": {
        "GITHUB_TOKEN": "your-token",
        "CODE_REVIEW_RULES_DIR": "/absolute/path/to/project/.code-review-rules"
      }
    }
  }
}

How It Works:

  • Set CODE_REVIEW_RULES_DIR to a directory containing .md or .mdc files

  • Or simply place a .code-review-rules/ directory in your project root (auto-discovered)

  • The get_review_rules tool returns both builtin and custom rules

  • AI assistants use these rules when performing reviews

  • Custom rules supplement (not replace) the builtin review guidelines

Environment Variable:

Variable

Description

Required

CODE_REVIEW_RULES_DIR

Path to custom rules directory

No (optional)

🀝 Contributing

Contributions welcome! See CONTRIBUTING.md for details.

πŸ“„ License

MIT

❓ FAQ

What is code-review-mcp?
An MCP server that lets AI assistants (Cursor, Claude, etc.) fetch PR/MR diffs and post inline or general review comments on GitHub and GitLab.

How do I install it?
Fastest: uvx code-review-mcp. Or pip install code-review-mcp. See Quick Start.

Does it work with self-hosted GitLab?
Yes. Set GITLAB_TOKEN and GITLAB_HOST=gitlab.yourcompany.com.

Which AI clients are supported?
Any MCP client β€” Cursor, Claude Desktop, and custom integrations via stdio, SSE, or WebSocket.

Can I add project-specific review rules?
Yes. Run code-review-mcp init-rules --custom or set CODE_REVIEW_RULES_DIR.

Is my code stored on a server?
No persistent storage. Tokens are read from environment variables; diffs are fetched on demand from GitHub/GitLab APIs.

Where can AI assistants read a structured summary?
See llms.txt in this repository.

Available Tools

7 tools
add_inline_commentA

Add inline comment to a specific code line in PR/MR

ParametersJSON Schema
NameRequiredDescriptionDefault
providerYesCode hosting provider
repoYesRepository path
pr_idYesPR/MR number
file_pathYesPath to the file
lineYesLine number to comment on
line_typeYesLine type: 'old' for deleted line, 'new' for added line
commentYesComment content
hostNoGitLab host for self-hosted instances

TDQS

A3.5/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate non-readonly, non-destructive behavior. The description adds no extra behavioral details beyond the core action. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single, clear sentence with no redundant words. Efficiently conveys the purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 8 parameters and no output schema, the description is minimal. It omits details like return values, permissions, or limitations, but the schema covers parameters fully.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description does not add any additional meaning beyond the schema's parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Add inline comment to a specific code line in PR/MR' uses a specific verb ('Add') and resource ('inline comment to a specific code line'), clearly distinguishing it from siblings like 'add_pr_comment' which targets general PR comments.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives such as 'add_pr_comment' or 'batch_add_comments'. The description lacks context on prerequisites or scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

add_pr_commentB

Add a general comment to PR/MR

ParametersJSON Schema
NameRequiredDescriptionDefault
providerYesCode hosting provider
repoYesRepository path
pr_idYesPR/MR number
commentYesComment content
hostNoGitLab host for self-hosted instances

TDQS

B3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint=false and destructiveHint=false, but the description does not elaborate on behavioral traits beyond stating the action. It lacks information about side effects, authorization needs, or idempotency. With no output schema, more detail on what happens after adding would help.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short and to the point, but it omits useful context that could be included without bloating. It achieves conciseness but at the cost of completeness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 5 parameters and no output schema, the description does not address what happens after a successful add, such as confirmation or potential limitations. It lacks completeness for a tool with moderate complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with each parameter having a clear description. The tool description adds no extra meaning beyond what is already in the schema, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Add a general comment to PR/MR' clearly specifies the action (add) and resource (comment on PR/MR). It differentiates from sibling tools like 'add_inline_comment' and 'batch_add_comments' by indicating it's a general comment.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, context, or exclusions for using this tool over others like 'batch_add_comments' or 'extract_related_prs'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

batch_add_commentsA

Batch add multiple inline comments and optionally a general comment

ParametersJSON Schema
NameRequiredDescriptionDefault
providerYesCode hosting provider
repoYesRepository path
pr_idYesPR/MR number
inline_commentsYesList of inline comments to add
pr_commentNoOptional general PR/MR comment
hostNoGitLab host for self-hosted instances

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate a non-read-only, non-destructive operation. The description confirms it is a write operation ('add'), which aligns. However, no extra behavioral details (e.g., side effects, error handling) are provided beyond the annotations. The description does not contradict annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that efficiently conveys the tool's primary function. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite the complexity of a batch operation and the lack of output schema, the description is minimal. It does not address partial success, error scenarios, or prerequisites like authentication. More context is needed for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, so each parameter is already documented. The description adds no additional parameter semantics beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it performs a batch operation for adding inline comments and optionally a general comment. This verb+resource combination (batch_add_comments) is specific and distinguishes it from sibling tools like add_inline_comment and add_pr_comment which are single operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for multiple inline comments but does not explicitly state when to use this tool versus alternatives. It lacks guidance on when not to use it (e.g., for a single comment) or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_pr_changesA
Read-only

Get PR/MR code changes (diff) with optional file extension filtering

ParametersJSON Schema
NameRequiredDescriptionDefault
providerYesCode hosting provider
repoYesRepository path
pr_idYesPR/MR number
hostNoGitLab host for self-hosted instances
file_extensionsNoFilter files by extensions (e.g., ['.py', '.js'])

TDQS

A3.5/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, so the description's 'Get... changes' is consistent. The description adds that it returns a diff and supports file filtering, but does not disclose behavior for errors, pagination, or performance. Some value added over annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, efficient and front-loaded with the core functionality. However, it could benefit from slightly more structure or elaboration without becoming verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, so the description should explain the return format (e.g., unified diff, list of changed files). It does not. With 5 parameters and no output context, the description is incomplete for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage with descriptions for all 5 parameters. The description only reiterates the filtering capability already present in the schema. No additional semantic value provided beyond the structured schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get PR/MR code changes (diff)' with optional file extension filtering, specifying the exact verb and resource. It is distinct from sibling tools like get_pr_info (metadata) and add_inline_comment (commenting).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives such as get_pr_info or extract_related_prs. The description implies usage for viewing diffs, but does not provide contextual or exclusionary advice.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_pr_infoA
Read-only

Get PR/MR detailed information including title, description, author, and branches

ParametersJSON Schema
NameRequiredDescriptionDefault
providerYesCode hosting provider (github or gitlab)
repoYesRepository path (e.g., owner/repo or group/project)
pr_idYesPR/MR number
hostNoGitLab host for self-hosted instances (optional, default: gitlab.com)

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already set readOnlyHint=true and openWorldHint=true. The description adds no further behavioral traits (e.g., error handling, pagination), but it does not contradict annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence is direct and complete, front-loading the purpose and output fields without any superfluous words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple retrieval tool with good schema coverage and annotations, the description sufficiently conveys what the tool returns. Minor gap: no mention of error cases or response format, but acceptable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema covers all 4 parameters with 100% description coverage. The description adds no additional meaning beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves detailed PR/MR information including specific fields (title, description, author, branches), distinguishing it from sibling tools like add_pr_comment or extract_related_prs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for fetching PR details but does not explicitly state when to use versus alternatives like get_pr_changes or add_inline_comment. However, the read-only annotation and result content provide enough context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_review_rulesA
Read-only

Get code review rules (builtin + custom project rules). Call this before starting a review to load all applicable rules. Custom rules are loaded from CODE_REVIEW_RULES_DIR env var, or auto-discovered from .code-review-rules/ in the working directory.

ParametersJSON Schema
NameRequiredDescriptionDefault
langNoLanguage filter for builtin rules (optional). 'zh' for Chinese, 'en' for English. If omitted, all builtin rules are returned.
include_builtinNoWhether to include builtin rules (default: true)

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint=true, indicating no mutation. The description adds behavioral context about custom rule loading (from env var or .code-review-rules/ directory), which is valuable beyond what annotations convey. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences: first states purpose, second provides usage context and custom rule discovery. No wasted words. Front-loaded with purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read tool with two optional parameters and good annotations, the description covers usage and custom rule sources adequately. Missing return format description, but not critical given the tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with both parameters fully described. The description does not add parameter-specific meaning beyond the schema. Baseline of 3 is appropriate since schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool gets code review rules (both builtin and custom). This is distinct from sibling tools that deal with comments, PR extraction, and changes, so the agent can easily differentiate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly advises calling this 'before starting a review to load all applicable rules'. This provides clear context for when to use. Does not enumerate alternatives or explicitly exclude usage cases, but the guidance is sufficient for a simple tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 7 tool updatesv1.2.2
    • First observedadd_inline_comment
    • First observedadd_pr_comment
    • First observedbatch_add_comments
    • First observedextract_related_prs
    • First observedget_pr_changes
    • First observedget_pr_info
    • First observedget_review_rules

TDQS

A3.8/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: inline comments, general comments, batch comments, extracting PR links, getting PR changes, PR info, and review rules. No ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., add_inline_comment, get_pr_changes), making them predictable and easy to understand.

Tool Count5/5

Seven tools is well-scoped for a code review server, covering commenting, information retrieval, and rule loading without unnecessary bloat.

Completeness4/5

The tool set covers core review operations (viewing info, changes, adding comments, rules), but lacks edit/delete comment functionality, which is a minor gap.

Maintenance

ActivityMaintained
ResponsivenessNo issues

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