AI Code Review MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| WORKSPACE_PATH | Yes | The local path to your workspace for file system operations | |
| GITHUB_PERSONAL_ACCESS_TOKEN | Yes | Your GitHub Personal Access Token |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| github_list_reposC | List GitHub repositories for authenticated user |
| github_get_prC | Get pull request details |
| github_get_pr_diffC | Get pull request diff for code review |
| github_list_prsC | List pull requests in a repository |
| fs_read_fileC | Read file contents from workspace |
| fs_list_filesC | List files in a directory |
| fs_search_filesC | Search for files by pattern (e.g., *.js, *.py) |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 7 tools
The tools are mostly distinct with clear purposes, but there is some potential overlap between fs_list_files and fs_search_files, as both involve listing files, which could cause minor confusion. However, their descriptions clarify that one lists all files while the other searches by pattern, helping to mitigate ambiguity.
The naming is mixed, with fs_* tools using a prefix and github_* tools using a different prefix, but within each group, the verb_noun pattern is consistent (e.g., fs_list_files, github_get_pr). This mixed convention reduces overall consistency, though it remains readable and functional.
With 7 tools, the count is reasonable for an AI code review server, covering file system operations and GitHub PR interactions. It is slightly lean but well-scoped, as each tool serves a specific purpose without obvious bloat or redundancy.
The tool surface has notable gaps for a code review domain, such as missing tools for creating or updating PRs, commenting on code, or analyzing code quality directly. While it covers basic file and PR retrieval, it lacks full lifecycle coverage, which could limit agent effectiveness in comprehensive code review workflows.