Install gemini-code-review-mcp from PyPI
Install exact version 0.5.0. The executable name has not been verified, so it is intentionally not guessed.
python -m pip install 'gemini-code-review-mcp==0.5.0'MCP server intelligence profile
Gemini Code Review MCP Server
AI-powered code review server that analyzes git diffs and PRs with context from project guidelines and task lists. Supports integration with Claude Code and Cursor via MCP
1Distribution channel
3Independently observed tools
0Linked remote endpoints
AvailableVersion intelligence
Detailed security scan evidence is not public for this MCP yet. Public identity, registry metadata, and independently observed protocol inventory remain available.
Install and connect
Installation and connection instructions are shown only when supported by retained package, repository, or endpoint evidence.
Identity
| Canonical slug | gemini-code-review-mcp-6890b841 | Deployment | Local Only |
|---|---|---|---|
| Canonical package | pypi:gemini-code-review-mcp | Repository | nicobailon/gemini-code-review-mcp |
| First published | Jun 15, 2025 | Latest release | Jun 15, 2025 |
| Last security verification | — | Classification confidence | 90% |
| Publication | Draft | Official distribution | Not verified |
Distributions
| Channel | Identifier | Current version | Versions | Source |
|---|---|---|---|---|
| pypi | gemini-code-review-mcp | 0.5.0 | 1 | Repository |
Current release
| Package | Version | Published / observed | Inventory | Security scan |
|---|---|---|---|---|
| pypigemini-code-review-mcp | 0.5.0Current | Jun 15, 2025 | 3 toolsSucceeded · 0 resources · 0 prompts | Evidence restricted |
Enterprise protection
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Current version evidence
No public current-version evidence is available yet.
Current protocol inventory
2025-06-18Negotiated protocol
MCP Server - Code Review Context GeneratorServer-reported name
6Capability groups
Aug 23, 2026Observed
Tools 3
| Tool | Category | Annotations | Risk |
|---|---|---|---|
ask_geminiGenerates context from files and sends it to Gemini for a response.
This tool combines context generation with a direct call to the Gemini API.Input schema{
"additionalProperties": false,
"properties": {
"user_instructions": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "The primary query or instructions for Gemini."
},
"file_selections": {
"anyOf": [
{
"items": {
"additionalProperties": true,
"type": "object"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional list of files/line ranges to include in the context."
},
"project_path": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional project root for relative paths."
},
"include_claude_memory": {
"default": true,
"type": "boolean",
"description": "Include CLAUDE.md files in context."
},
"include_cursor_rules": {
"default": false,
"type": "boolean",
"description": "Include Cursor rules files in context."
},
"auto_meta_prompt": {
"default": true,
"type": "boolean",
"description": "If no user_instructions, generate a meta-prompt."
},
"temperature": {
"default": 0.5,
"type": "number",
"description": "AI temperature for generation."
},
"model": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Specific Gemini model to use."
},
"thinking_budget": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional token budget for thinking mode."
},
"text_output": {
"default": true,
"type": "boolean",
"description": "If True, return the response as a string. If False, save it to a file."
}
},
"type": "object"
}Output schema{
"properties": {
"result": {
"type": "string"
}
},
"required": [
"result"
],
"type": "object",
"x-fastmcp-wrap-result": true
} | — | — | |
generate_ai_code_reviewGenerate AI-powered code review from context file, content, or project analysis.Input schema{
"additionalProperties": false,
"properties": {
"context_file_path": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Path to existing code review context file (.md)"
},
"context_content": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Direct context content (for AI agent chaining)"
},
"project_path": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Project path for direct analysis (generates context internally)"
},
"scope": {
"default": "recent_phase",
"type": "string",
"description": "Review scope when using project_path - 'recent_phase', 'full_project', 'specific_phase', 'specific_task'"
},
"phase_number": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Phase number for specific_phase scope"
},
"task_number": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Task number for specific_task scope"
},
"task_list": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Specific task list file to use (overrides automatic discovery)"
},
"default_prompt": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Custom default prompt when no task list exists"
},
"output_path": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Custom output file path for AI review. If not provided, uses default timestamped path"
},
"model": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional Gemini model name (e.g., 'gemini-2.0-flash-exp', 'gemini-1.5-pro')"
},
"temperature": {
"default": 0.5,
"type": "number",
"description": "Temperature for AI model (default: 0.5, range: 0.0-2.0)"
},
"custom_prompt": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional custom AI prompt to override default instructions"
},
"text_output": {
"default": true,
"type": "boolean",
"description": "Return review directly as text (default: true - for AI agent chaining)"
},
"auto_meta_prompt": {
"default": true,
"type": "boolean",
"description": "Automatically generate and embed meta prompt (default: true)"
},
"include_claude_memory": {
"default": true,
"type": "boolean",
"description": "Include CLAUDE.md files in context (default: true)"
},
"include_cursor_rules": {
"default": false,
"type": "boolean",
"description": "Include Cursor rules files in context (default: false)"
},
"thinking_budget": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional token budget for thinking mode (if supported by model)"
},
"url_context": {
"anyOf": [
{
"type": "string"
},
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional URL(s) to include in context - can be string or list of strings"
}
},
"type": "object"
}Output schema{
"properties": {
"result": {
"type": "string"
}
},
"required": [
"result"
],
"type": "object",
"x-fastmcp-wrap-result": true
} | — | — | |
generate_pr_reviewGenerate code review for a GitHub Pull Request with configuration discovery.Input schema{
"additionalProperties": false,
"properties": {
"github_pr_url": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "GitHub PR URL (e.g., 'https://github.com/owner/repo/pull/123')"
},
"project_path": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional local project path for context (default: current directory)"
},
"temperature": {
"default": 0.5,
"type": "number",
"description": "Temperature for AI model (default: 0.5, range: 0.0-2.0)"
},
"enable_gemini_review": {
"default": true,
"type": "boolean",
"description": "Enable Gemini AI code review generation (default: true)"
},
"include_claude_memory": {
"default": true,
"type": "boolean",
"description": "Include CLAUDE.md files in context (default: true)"
},
"include_cursor_rules": {
"default": false,
"type": "boolean",
"description": "Include Cursor rules files in context (default: false)"
},
"auto_meta_prompt": {
"default": true,
"type": "boolean",
"description": "Automatically generate and embed meta prompt in user_instructions (default: true)"
},
"use_templated_instructions": {
"default": false,
"type": "boolean",
"description": "Use templated backup instructions instead of generated meta prompt (default: false)"
},
"create_context_file": {
"default": false,
"type": "boolean",
"description": "Save context to file and return context content (default: false)"
},
"raw_context_only": {
"default": false,
"type": "boolean",
"description": "Return raw context content without AI processing (default: false)"
},
"text_output": {
"default": false,
"type": "boolean",
"description": "Return content directly without saving (default: false - saves to timestamped .md file)"
},
"thinking_budget": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional token budget for thinking mode (if supported by model)"
},
"url_context": {
"anyOf": [
{
"type": "string"
},
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional URL(s) to include in context - can be string or list of strings"
}
},
"type": "object"
}Output schema{
"properties": {
"result": {
"type": "string"
}
},
"required": [
"result"
],
"type": "object",
"x-fastmcp-wrap-result": true
} | — | — |
Resources 0
- None observed.
Resource templates 0
- None observed.
Prompts 0
- None observed.
Remote endpoints
| Endpoint | Transport | Authentication | Health | Observed |
|---|---|---|---|---|
| No verified remote endpoint is linked. | ||||
Gemini Code Review MCP Server questions
How do I install Gemini Code Review MCP Server?
Install the selected package version with: python -m pip install 'gemini-code-review-mcp==0.5.0'
What tools does Gemini Code Review MCP Server provide?
Gemini Code Review MCP Server exposed 3 tools during independent protocol observation, including ask_gemini, generate_ai_code_review, generate_pr_review.
Is Gemini Code Review MCP Server secure?
The selected current version does not yet have completed public verification. Unknown does not mean clean or vulnerable.
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