shariqriazz/vertex-ai-mcp-server
vertex-ai-mcp-server is hosted online, so all tools can be tested directly either in theInspector tabor in theOnline Client.
If you are the rightful owner of vertex-ai-mcp-server and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.
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.
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Tools
Functions exposed to the LLM to take actions
answer_query_websearch
Answers a natural language query using the configured Vertex AI model (gemini-2.0-flash) enhanced with Google Search results for up-to-date information. Requires a 'query' string.
answer_query_direct
Answers a natural language query using only the internal knowledge of the configured Vertex AI model (gemini-2.0-flash). Does not use web search. Requires a 'query' string.
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. Focuses on comprehensive answers, context, and adherence to documented details. Uses the configured Vertex AI model (gemini-2.0-flash) with Google Search. Requires 'topic' and 'query'.
get_doc_snippets
Provides precise, authoritative code snippets or concise answers for technical queries by searching official documentation. Focuses on delivering exact solutions without unnecessary explanation. Uses the configured Vertex AI model (gemini-2.0-flash) with Google Search. Requires 'topic' and 'query'.
generate_project_guidelines
Generates a structured project guidelines document (e.g., Markdown) based on a specified list of technologies and versions (tech stack). Uses web search to find the latest official documentation, style guides, and best practices for each component and synthesizes them into actionable rules and recommendations. Uses the configured Vertex AI model (gemini-2.0-flash) with Google Search. Requires 'tech_stack'.
read_file_content
Read the complete contents of one or more files from the workspace filesystem. Provide a single path string or an array of path strings. Handles various text encodings and provides detailed error messages if a file cannot be read. Failed reads for individual files in an array won't stop the entire operation when multiple paths are provided.
write_file_content
Create new files or completely overwrite existing files in the workspace filesystem. The 'writes' argument should be either a single object with 'path' and 'content', or an array of such objects to write multiple files. Use with caution as it will overwrite existing files without warning. Handles text content with proper encoding.
edit_file_content
Make line-based edits to a text file in the workspace filesystem. Each edit attempts to replace an exact match of 'oldText' with 'newText'. If no exact match is found, it attempts a line-by-line match ignoring leading/trailing whitespace. Indentation of the first line is preserved, and relative indentation of subsequent lines is attempted. Returns a git-style diff showing the changes made (or previewed if dryRun is true).
list_directory_contents
Get a detailed listing of all files and directories directly within a specified path in the workspace filesystem. Results clearly distinguish between files and directories with [FILE] and [DIR] prefixes. This tool is essential for understanding directory structure and finding specific files within a directory. Does not list recursively.
get_directory_tree
Get a recursive tree view of files and directories within the workspace filesystem as a JSON structure. Each entry includes 'name', 'type' (file/directory), and 'children' (an array) for directories. Files have no 'children' array. The output is formatted JSON text. Useful for understanding the complete structure of a project directory.
move_file_or_directory
Move or rename files and directories within the workspace filesystem. Can move items between directories and rename them in a single operation. If the destination path already exists, the operation will likely fail (OS-dependent).
search_filesystem
Recursively search for files and directories within the workspace filesystem matching a pattern in their name. Searches through all subdirectories from the starting path. The search is case-insensitive and matches partial names. Returns full paths (relative to workspace) to all matching items. Supports excluding paths using glob patterns.
get_filesystem_info
Retrieve detailed metadata about a file or directory within the workspace filesystem. Returns comprehensive information including size (bytes), creation time, last modified time, last accessed time, type (file/directory), and permissions (octal string). This tool is perfect for understanding file characteristics without reading the actual content.
execute_terminal_command
Execute a shell command on the server's operating system. Allows specifying the command, an optional working directory (cwd), and an optional timeout in seconds. Returns the combined stdout and stderr output of the command upon completion or termination.
save_generate_project_guidelines
Generates comprehensive project guidelines based on a tech stack using web search and saves the result to a specified file path. Uses the configured Vertex AI model (gemini-2.0-flash). Requires 'tech_stack' and 'output_path'.
save_doc_snippet
Provides precise code snippets or concise answers for technical queries by searching official documentation and saves the result to a file. Uses the configured Vertex AI model (gemini-2.0-flash) with Google Search. Requires 'topic', 'query', and 'output_path'.
save_topic_explanation
Provides a detailed explanation for a query about a specific software topic using official documentation found via web search and saves the result to a file. Uses the configured Vertex AI model (gemini-2.0-flash). Requires 'topic', 'query', and 'output_path'.
save_answer_query_direct
Answers a natural language query using only the internal knowledge of the configured Vertex AI model (gemini-2.0-flash), does not use web search, and saves the answer to a file. Requires 'query' and 'output_path'.
save_answer_query_websearch
Answers a natural language query using Google Search results and saves the answer to a file. Uses the configured Vertex AI model (gemini-2.0-flash). Requires 'query' and 'output_path'.
code_analysis_with_docs
Analyzes code snippets by comparing them with best practices from official documentation found via web search. Identifies potential bugs, performance issues, and security vulnerabilities. Uses the configured Vertex AI model (gemini-2.0-flash) with Google Search. Requires 'code', 'language', and 'analysis_focus'.
technical_comparison
Compares multiple technologies, frameworks, or libraries based on specific criteria. Provides detailed comparison tables with pros/cons and use cases. Includes version-specific information and compatibility considerations. Uses the configured Vertex AI model (gemini-2.0-flash) with Google Search. Requires 'technologies' and 'criteria'.
architecture_pattern_recommendation
Suggests architecture patterns for specific use cases based on industry best practices. Provides implementation examples and considerations for the recommended patterns. Includes diagrams and explanations of pattern benefits and tradeoffs. Uses the configured Vertex AI model (gemini-2.0-flash) with Google Search. Requires 'requirements' and 'tech_stack'.
dependency_vulnerability_scan
Analyzes project dependencies for known security vulnerabilities. Provides detailed information about each vulnerability with severity ratings. Suggests mitigation strategies and secure alternatives. Uses the configured Vertex AI model (gemini-2.0-flash) with Google Search. Requires 'dependencies' and 'ecosystem'.
database_schema_analyzer
Reviews database schemas for normalization, indexing, and performance issues. Suggests improvements based on database-specific best practices. Provides migration strategies for implementing suggested changes. Uses the configured Vertex AI model (gemini-2.0-flash) with Google Search. Requires 'schema' and 'database_type'.
security_best_practices_advisor
Provides security recommendations for specific technologies or scenarios. Includes code examples for implementing secure practices. References industry standards and security guidelines. Uses the configured Vertex AI model (gemini-2.0-flash) with Google Search. Requires 'technology' and 'security_context'.
testing_strategy_generator
Creates comprehensive testing strategies for applications or features. Suggests appropriate testing types (unit, integration, e2e) with coverage goals. Provides example test cases and testing frameworks. Uses the configured Vertex AI model (gemini-2.0-flash) with Google Search. Requires 'project_description' and 'tech_stack'.
regulatory_compliance_advisor
Provides guidance on regulatory requirements for specific industries (GDPR, HIPAA, etc.). Suggests implementation approaches for compliance. Includes checklists and verification strategies. Uses the configured Vertex AI model (gemini-2.0-flash) with Google Search. Requires 'regulations' and 'context'.
microservice_design_assistant
Helps design microservice architectures for specific domains. Provides service boundary recommendations and communication patterns. Includes deployment and orchestration considerations. Uses the configured Vertex AI model (gemini-2.0-flash) with Google Search. Requires 'domain_description' and 'requirements'.
documentation_generator
Creates comprehensive documentation for code, APIs, or systems. Follows industry best practices for technical documentation. Includes examples, diagrams, and user guides. Uses the configured Vertex AI model (gemini-2.0-flash) with Google Search. Requires 'content_type' and 'content'.
Prompts
Interactive templates invoked by user choice
No prompts
Resources
Contextual data attached and managed by the client
