A Model Context Protocol server supporting Vertex AI and Gemini API
A Model Context Protocol server supporting Vertex AI and Gemini API
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.
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).Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.
Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| 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. |
{
"mcpServers": {
"vertex-ai": {
"command": "npx",
"args": ["-y", "bun"],
"env": {
"AI_PROVIDER": "your-value",
"GOOGLE_CLOUD_PROJECT": "your-value",
"GEMINI_API_KEY": "your-value",
"VERTEX_MODEL_ID": "your-value",
"GEMINI_MODEL_ID": "your-value",
"GOOGLE_CLOUD_LOCATION": "your-value",
"AI_TEMPERATURE": "your-value",
"AI_USE_STREAMING": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
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).| Variable | Description | Required |
|---|---|---|
| AI_PROVIDER | Configuration value read at startup. | Optional |
| GOOGLE_CLOUD_PROJECT | Configuration value read at startup. | Optional |
| GEMINI_API_KEY | Credential the server authenticates with. | Yes |
| VERTEX_MODEL_ID | Configuration value read at startup. | Optional |
| GEMINI_MODEL_ID | Configuration value read at startup. | Optional |
| GOOGLE_CLOUD_LOCATION | Configuration value read at startup. | Optional |
| AI_TEMPERATURE | Configuration value read at startup. | Optional |
| AI_USE_STREAMING | Configuration value read at startup. | Optional |
Inspect PostgreSQL schemas and run read-only queries with the archived MCP reference server. Review its maintenance status before choosing it.
Connect the Notion MCP server to search workspace content and work with pages and databases through Notion’s hosted OAuth service.
Use the Supabase MCP server to inspect database schemas, query project data and retrieve docs through a project-scoped connection.
Query, modify and analyse local SQLite databases in conversation — the fastest way to chat with a data file.
Metabase ships its own MCP endpoint — search your BI content, build and run queries, and save questions and dashboards without leaving the chat.
Official MongoDB server covering data, schemas and Atlas management — from find queries to spinning up clusters.