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.

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

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