A Model Context Protocol server supporting Vertex AI and Gemini API
Vertex Ai MCP server exists for a simple reason — assistants are far more useful when they can act on Vertex Ai directly instead of describing what you should do. 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.
Once Vertex Ai is connected, these are the calls the assistant has available:
code_analysis_with_docs — Analyzes code snippets by comparing them with best practices from official documentation, identifying potential bugs, performance issues, andtechnical_comparison — Compares multiple technologies, frameworks, or libraries based on specific criteria, providing detailed comparison tables with pros/cons and usearchitecture_pattern_recommendation — Suggests architecture patterns for specific use cases based on industry best practices, with implementation examples and considerationsdependency_vulnerability_scan — Analyzes project dependencies for known security vulnerabilities, providing detailed information and mitigation strategiesdatabase_schema_analyzer — Reviews database schemas for normalization, indexing, and performance issues, suggesting improvements based on database-specific best practicessecurity_best_practices_advisor — Provides security recommendations for specific technologies or scenarios, with code examples for implementing secure practicestesting_strategy_generator — Creates comprehensive testing strategies for applications or features, suggesting appropriate testing types with coverage goalsregulatory_compliance_advisor — Provides guidance on regulatory requirements for specific industries (GDPR, HIPAA, etc.), with implementation approaches for compliancemicroservice_design_assistant — Helps design microservice architectures for specific domains, with service boundary recommendations and communication patternsdocumentation_generator — Creates comprehensive documentation for code, APIs, or systems, following industry best practices for technical documentationYou will need 8 environment variables: AI_PROVIDER, GOOGLE_CLOUD_PROJECT, GEMINI_API_KEY, VERTEX_MODEL_ID, GEMINI_MODEL_ID, GOOGLE_CLOUD_LOCATION, AI_TEMPERATURE, AI_USE_STREAMING. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
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).bun on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Plenty of knowledge and memory servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Vertex Ai's toolset — code_analysis_with_docs, technical_comparison, architecture_pattern_recommendation and 7 more — is a fair guide to whether it matches your workflow. It is maintained by shariqriazz; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| 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 |
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.