Google AI Search MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| AI_PROVIDER | No | The AI provider to use. Either 'vertex' for Vertex AI or 'gemini' for Gemini API | |
| AI_MAX_RETRIES | No | Maximum number of retries for transient API errors | 3 |
| AI_TEMPERATURE | No | The temperature parameter for AI generation | 0.0 |
| GEMINI_API_KEY | No | Your Gemini API Key (required if AI_PROVIDER='gemini') | |
| GEMINI_MODEL_ID | No | The Gemini model ID to use (if AI_PROVIDER='gemini') | gemini-2.5-pro |
| VERTEX_MODEL_ID | No | The Vertex AI model ID to use (if AI_PROVIDER='vertex') | gemini-2.5-pro |
| AI_USE_STREAMING | No | Whether to use streaming API for AI responses | true |
| AI_RETRY_DELAY_MS | No | Delay in milliseconds between retries | 1000 |
| AI_MAX_OUTPUT_TOKENS | No | Maximum number of output tokens for AI generation | 65536 |
| GOOGLE_CLOUD_PROJECT | No | Your GCP Project ID (required if AI_PROVIDER='vertex') | |
| GOOGLE_CLOUD_LOCATION | No | The Google Cloud location/region (specific to Vertex AI) | us-central1 |
| GOOGLE_APPLICATION_CREDENTIALS | No | Path to your service account key JSON file (if using Service Account Key for Vertex authentication) |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| answer_query_websearchB | Answers a natural language query using the configured Vertex AI model (gemini-2.5-pro) enhanced with Google Search results for up-to-date information. Requires a 'query' string. |
| explain_topic_with_docsA | 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-2.5-pro) with Google Search. Requires 'topic' and 'query'. |
| get_doc_snippetsB | 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-2.5-pro) with Google Search. Requires 'topic' and 'query'. |
| generate_project_guidelinesA | 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-2.5-pro) with Google Search. Requires 'tech_stack'. |
| code_analysis_with_docsA | 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-2.5-pro) with Google Search. Requires 'code', 'language', and 'analysis_focus'. |
| technical_comparisonA | 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-2.5-pro) with Google Search. Requires 'technologies' and 'criteria'. |
| architecture_pattern_recommendationA | 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-2.5-pro) with Google Search. Requires 'requirements' and 'tech_stack'. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 7 tools
Most tools have distinct purposes, but answer_query_websearch and explain_topic_with_docs overlap semantically enough that an agent may hesitate between them. The shared boilerplate descriptions also reduce clarity across several tools.
Several tools follow a verb_object pattern, but code_analysis_with_docs, technical_comparison, and architecture_pattern_recommendation are noun-phrase names. This mixed convention is still readable but not consistently applied.
Seven tools is a well-scoped count for a domain-specific AI search and documentation assistant. Each tool covers a meaningful high-level task without unnecessary redundancy or bloat.
The toolset covers Q&A, documentation explanation, snippets, code analysis, comparisons, architecture planning, and guideline generation. A raw search or retrieval tool is absent, but not a major gap given the AI-assisted focus.