Code Review MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_API_KEY | Yes | Your OpenAI API key |
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 |
|---|---|
| review_codeC | Review code changes and provide feedback |
| analyze_securityC | Analyze code for security vulnerabilities |
| suggest_improvementsC | Suggest code quality improvements |
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 3 tools
review_code is broad and overlaps with suggest_improvements (both provide code quality feedback) and partially with analyze_security. The descriptions help but do not fully draw clear boundaries between general review and targeted analysis.
All three tools follow a consistent verb_noun snake_case pattern: review_code, analyze_security, suggest_improvements. The convention is predictable and readable.
Three tools is reasonable for a focused code-review server, with each targeting a distinct analysis angle. It is slightly thin for a full code-review workflow (e.g., no diff retrieval or comment posting), but not problematic.
The surface covers three common analysis types but lacks lifecycle operations such as fetching code changes, retrieving diffs, or posting review comments. These gaps may force agents to combine with other tools to complete a review workflow.