Vertex AI MCP Server
About
[](https://mseep.ai/app/shariqriazz-vertex-ai-mcp-server) # Vertex AI MCP Server [
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Vertex AI MCP ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install dependencies with bun install, build with bun run build, then run via bunx vertex-ai-mcp-server or configure it in your MCP client (e.g., Cline) by specifying the command and environment variables. Required configurations include setting AI_PROVIDER and either GOOGLE_CLOUD_PROJECT (for Vertex) or GEMINI_API_KEY (for Gemini).
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"vertex ai mcp server": {
"vertex-ai-mcp-server": {
"command": "bun",
"args": [
"run",
"build"
]
}
}
}
}McpServers
{
"vertex-ai-mcp-server": {
"command": "bun",
"args": [
"run",
"build"
]
}
}Vertex AI MCP Server
This project implements a Model Context Protocol (MCP) server that provides a comprehensive suite of tools for interacting with Google Cloud's Vertex AI Gemini models, focusing on coding assistance and general query answering.
<a href="https://glama.ai/mcp/servers/@shariqriazz/vertex-ai-mcp-server">
</a>
Features
Provides access to Vertex AI Gemini models via numerous MCP tools.
Supports web search grounding (answer_query_websearch) and direct knowledge answering (answer_query_direct).
Configurable model ID, temperature, streaming behavior, max output tokens, and retry settings via environment variables.
Uses streaming API by default for potentially better responsiveness.
Includes basic retry logic for transient API errors.
Minimal safety filters applied (BLOCK_NONE) to reduce potential blocking (use with caution).
Tools Provided
Query & Generation (AI Focused)
answer_query_websearch: Answers a natural language query using the configured Vertex AI model enhanced with Google Search results.
answer_query_direct: Answers a natural language query using only the internal knowledge of the configured Vertex AI model.
explain_topic_with_docs: Provides a detailed explanation for a query about a specific software topic by synthesizing information primarily from official documentation found via web search.
get_doc_snippets: Provides precise, authoritative code snippets or concise answers for technical queries by searching official documentation.
generate_project_guidelines: Generates a structured project guidelines document (Markdown) based on a specified list of technologies (optionally with versions), using web search for best practices.
Research & Analysis Tools
code_analysis_with_docs: Analyzes code snippets by comparing them with best practices from official documentation, identifying potential bugs, performance issues, and security vulnerabilities.
technical_comparison: Compares multiple technologies, frameworks, or libraries based on specific criteria, providing detailed comparison tables with pros/cons and use cases.
architecture_pattern_recommendation: Suggests architecture patterns for specific use cases based on industry best practices, with implementation examples and considerations.
dependency_vulnerability_scan: Analyzes project dependencies for known security vulnerabilities, providing detailed information and mitigation strategies.
database_schema_analyzer: Reviews database schemas for normalization, indexing, and performance issues, suggesting improvements based on database-specific best practices.
security_best_practices_advisor: Provides security recommendations for specific technologies or scenarios, with code examples for implementing secure practices.
testing_strategy_generator: Creates comprehensive testing strategies for applications or features, suggesting appropriate testing types with coverage goals.
regulatory_compliance_advisor: Provides guidance on regulatory requirements for specific industries (GDPR, HIPAA, etc.), with implementation approaches for compliance.
microservice_design_assistant: Helps design microservice architectures for specific domains, with service boundary recommendations and communication patterns.
documentation_generator: Creates comprehensive documentation for code, APIs, or systems, following industry best practices for technical documentation.
Filesystem Operations
read_file_content: Read the complete contents of one or more files. Provide a single path string or an array of path strings.
write_file_content: Create new files or completely overwrite existing files. The 'writes' argument accepts a single object ({path, content}) or an array of such objects.
edit_file_content: Makes line-based edits to a text file, returning a diff preview or applying changes.
list_directory_contents: Lists files and directories directly within a specified path (non-recursive).
get_directory_tree: Gets a recursive tree view of files and directories as JSON.
move_file_or_directory: Moves or renames files and directories.
search_filesystem: Recursively searches for files/directories matching a name pattern, with optional exclusions.
get_filesystem_info: Retrieves detailed metadata (size, dates, type, permissions) about a file or directory.
execute_terminal_command: Execute a shell command, optionally specifying cwd and timeout. Returns stdout/stderr.
Combined AI + Filesystem Operations
save_generate_project_guidelines: Generates project guidelines based on a tech stack and saves the result to a specified file path.
save_doc_snippet: Finds code snippets from documentation and saves the result to a specified file path.
save_topic_explanation: Generates a detailed explanation of a topic based on documentation and saves the result to a specified file path.
save_answer_query_direct: Answers a query using only internal knowledge and saves the answer to a specified file path.
save_answer_query_websearch: Answers a query using web search results and saves the answer to a specified file path.
(Note: Input/output schemas for each tool are defined in their respective files within src/tools/ and exposed via the MCP server.)
Prerequisites
Node.js (v18+)Bun (
npm install -g bun)Google Cloud Project with Billing enabled.
Vertex AI API enabled in the GCP project.
Google Cloud Authentication configured in your environment (Application Default Credentials via
gcloud auth application-default login is recommended, or a Service Account Key).
Setup & Installation
1. Clone/Place Project: Ensure the project files are in your desired location.
2. Install Dependencies:
bun install
3. Configure Environment:
Create a
.env file in the project root (copy .env.example).Set the required and optional environment variables as described in
.env.example.Set
AI_PROVIDER to either "vertex" or "gemini".If
AI_PROVIDER="vertex", GOOGLE_CLOUD_PROJECT is required.* If
AI_PROVIDER="gemini", GEMINI_API_KEY is required.4. Build the Server:
bun run build
This compiles the TypeScript code to
build/index.js.
Usage (Standalone / NPX)
Once published to npm, you can run this server directly using npx:
```bash
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