Gemini Enterprise MCP Server
Configures and manages a SaaS connector for Confluence, enabling synchronization state management and data ingestion.
Provides integration with Google Cloud Discovery Engine for semantic search, document retrieval, datastore management, app configuration, and other enterprise search features.
Configures and manages a SaaS connector for Jira, enabling synchronization state management and data ingestion.
Configures and manages a SaaS connector for Salesforce, enabling synchronization state management and data ingestion.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Gemini Enterprise MCP Serversearch for 'onboarding guide' in the datastore"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
π Gemini Enterprise MCP Server & Agent Skills
Welcome to the Gemini Enterprise MCP Server.
Unofficial Project: This is NOT an official Google repository or product. It is an absolutely unofficial, personal initiative created to support and facilitate the adoption of Gemini Enterprise.
This open-source project (released under the Apache 2.0 license) implements a Model Context Protocol (MCP) server in TypeScript/ESM to connect AI coding assistants and orchestration frameworks with the powerful Google Cloud Discovery Engine APIs (which power Gemini Enterprise App).
The server handles both Data Plane operations (semantic search, conversational RAG with the Answer API, document retrieval) and Control Plane operations (automated datastore creation, JSON schema management, ranking/Serving Configs tuning, and SaaS connector configurations).
π Key Features & API Coverage
The server provides complete (100% operational coverage) mapping to the core gRPC/REST endpoints of Google Cloud Discovery Engine:
π Data Plane (Semantic Search & RAG)
Advanced Search (
gemini_enterprise_search): Executes semantic queries on datastores, returning titles, snippets, URIs, and structured data formatted in clean Markdown for your agents.Conversational Search (
gemini_enterprise_ask): Queries the Answer API (Conversational RAG) receiving grounding answers, confidence scores, and structured references.Document Retrieval (
gemini_enterprise_get_document): Allows the agent to download the entire contents and structured metadata of an indexed document for in-depth analysis.
βοΈ Control Plane (Administration & Tuning)
App & Engine Management (
gemini_enterprise_manage_apps): Complete CRUD and IAM operations to create, configure, retrieve, and set access policies for search and chat applications. LRO is fully supported.DataStore Management (
gemini_enterprise_manage_datastore): Instantiation and deletion of structured and unstructured Data Stores with custom industry verticals (e.g.,GENERIC,MEDIA).Advanced Web Search (
gemini_enterprise_manage_target_sites): Management of target sites (URLs and glob patterns) for automatic website crawling.Bulk Document Ingestion (
gemini_enterprise_manage_documents): Asynchronous ingestion and purging of documents from Cloud Storage (gs://...) or BigQuery sources.Custom Schema Management (
gemini_enterprise_manage_schema): Upload, modification, and retrieval of JSON schemas for indexing structured documents.Tuning & Controls (
gemini_enterprise_manage_controls): Creation of boost/bury rules, synonyms, redirects, and filters on Serving Configs.Feature Management (
gemini_enterprise_manage_features): Programmatic toggling of end-user Web App capabilities (Agent Gallery, Agent Designer, Prompt Gallery, Model Selector, NotebookLM, Session Sharing, Memory).Model Availability (
gemini_enterprise_manage_models): Management of Gemini model availability toggles (Gemini 2.5 Pro, 3.1 Pro, 3.5 Flash, 3.6 Flash) with bulk controls.Search UI & Shortcuts (
gemini_enterprise_manage_search_ui): Management of autocomplete suggestions, user feedback rating widgets, and pinned link shortcuts.Assistant & Prompts (
gemini_enterprise_manage_assistant): Configuration of prompt chips, custom system instructions, web grounding, location context, banned phrases, and chat retention.Observability & Analytics (
gemini_enterprise_manage_observability): OpenTelemetry instrumentation, prompt logging, and query analytics tracking management.Knowledge Graph (
gemini_enterprise_manage_knowledge_graph): Configuration of Google Cloud Knowledge Graph and Private Knowledge Graph with bulk controls.SaaS Connectors (
gemini_enterprise_configure_connector): High-fidelity simulated flow with synchronization state management for Jira, Salesforce, and Confluence.Agent Management (
gemini_enterprise_manage_agents): End-to-end CRUD operations (create, list, delete, update) for native, server-side Gemini Enterprise Agents.License & Billing Management (
gemini_enterprise_manage_licenses): Programmatic management of user seats on Gemini Enterprise / Gemini Code Assist license pools.
Related MCP server: BigQuery MCP Server
π οΈ Installation & Setup
1. Prerequisites
Node.js: v18.x or higher.
Google Cloud SDK (
gcloud) installed and configured.An active Google Cloud project with the Discovery Engine API enabled.
2. Local Authentication
Configure your local Application Default Credentials (ADC) pointing to your target GCP project:
gcloud auth application-default login3. Environment Variables
Create a .env file in the root of the project (or set variables in your shell/IDE environment):
GCP_PROJECT=your-gcp-project-id
GCP_LOCATION=global # optional, defaults to global (supports us, eu, etc.)
GCP_COLLECTION=default_collection # optional, defaults to default_collection
# OPTIONAL BUT RECOMMENDED:
# Set this variable if you encounter a local quota/billing project mismatch error from gcloud.
GOOGLE_CLOUD_QUOTA_PROJECT=your-gcp-project-id
# SECURITY SCOPES (Enforces Least Privilege):
# Comma-separated list of allowed scopes: search, admin, billing.
# Example: Use 'search' for standard coding assistants, and 'search,admin' for SRE agents.
# Defaults to 'search,admin,billing' if not specified.
MCP_SCOPES=search,admin,billingQuota & Billing Troubleshooting:
If during execution you receive an error like 7 PERMISSION_DENIED: Discovery Engine API has not been used in project..., your local user credentials are trying to attribute billing quotas to a disabled local sandbox project. Setting GOOGLE_CLOUD_QUOTA_PROJECT forces the SDK to attribute quotas to the correct project.
4. Build the Project
Install dependencies and compile TypeScript files into JavaScript ESM:
npm install
npm run buildπ§ͺ Testing Suite
To ensure stability and facilitate onboarding, the repository includes a suite of testing scripts under the tests/ directory to validate connectivity, security, and the MCP interface:
test_mcp_client.mjs(MCP Protocol Handshake): Performs a standard handshake with the MCP server via stdio and prints the list of registered tools with their JSON schemas.node tests/test_mcp_client.mjstest_suite_readonly.mjs(GCP Read-Only Validation): Scans the available datastores in the configured GCP project, querying schemas, target sites, serving controls, and testing search queries.node tests/test_suite_readonly.mjstest_admin_write.mjs(GCP Control Plane Safe Write/Delete): Executes a complete read/write lifecycle on the Control Plane (creates a temporary synonym control, verifies its existence via list, and immediately deletes it). 100% safe with zero residue left on your Cloud project.node tests/test_admin_write.mjstest_agents_write.mjs(Native Agent Lifecycle Safe Write/Delete): Executes a complete lifecycle check on native Discovery Engine Agents (creates a temporary agent, lists and updates it, then deletes it with zero residues remaining).node tests/test_agents_write.mjstest_iam.mjs(App IAM Policy Roundtrip Validation): Verifies that retrieving and setting IAM policies on individual Engines/Apps works correctly and safely.node tests/test_iam.mjstest_mcp_scopes.mjs(Scope & Security Enforcement Suite): Validates that environment-based tool restrictions (MCP_SCOPES) work correctly. It testssearchonly,search,billing, and full scopes, verifying that unauthorized attempts to call out-of-scope tools are securely rejected with access denied responses.node tests/test_mcp_scopes.mjs
π Running & Installing the Server
The MCP server communicates via stdio transport. You can easily register it, compile it locally, or automate the entire setup.
β‘ Option A: Automatic Installation (Highly Recommended)
You can automatically add and register ge-app-mcp in your favorite AI coding assistant (Claude Desktop, Cursor, Claude Code, etc.) using the open-source add-mcp utility:
npx add-mcp ge-app-mcp --env "GCP_PROJECT=your-gcp-project-id" --env "MCP_SCOPES=search,admin,billing"(This automatically discovers your installed AI agents, configures their configuration files, and passes the required environment variables securely.)
Option B: Manual Execution via npx
To run the server directly from npm without cloning:
npx -y ge-app-mcpOption C: Compile and Run Locally
# Clone, install dependencies, compile and start:
npm install
npm run build
npm start
# In development with hot-reloading:
npm run devπ§© Managing Pre-Built Agent Skills
This project fully supports the open Agent Skills specification (compatible with Vercel's skills CLI and standard coding agents). You can manage them in two ways:
β‘ Option A: Automatic via Vercel's skills CLI (Recommended)
You can instantly install and load all our pre-built enterprise skills into your workspace using Vercel's skills CLI:
npx skills add miticojo/ge-app-mcpOption B: Handled via the Local package CLI
If you prefer, you can use the built-in CLI utility bundled with our package to list and copy skills manually:
1. List Available Pre-Built Skills
npx ge-app-mcp skills list2. Install a Pre-Built Skill
Copy a pre-built skill folder into your local project workspace (defaults to ./skills/ if target directory is omitted):
npx ge-app-mcp skills install <skill-id> [target-directory]
# Example:
npx ge-app-mcp skills install admin-assistant ./my-skillsπ€ AI Agents & IDE Integration (Cursor, Windsurf, Claude Desktop, etc.)
For detailed, copy-paste configurations to integrate this server into your preferred AI agent workflows, see our dedicated integration guide:
π AGENTS.md
It provides comprehensive configurations for:
Cursor / Windsurf / Claude Desktop (Direct stdio setup using
npx ge-app-mcp).Google Agent Development Kit (ADK) (Native orchestration with
MCPToolset).LangChain & LangGraph (Python & TypeScript integrations).
CrewAI.
π Project Structure
ge-app-mcp/
βββ LICENSE # Apache 2.0 License
βββ README.md # This file
βββ AGENTS.md # Integration guide for AI Agents (ADK, LangChain, Cursor)
βββ package.json
βββ tsconfig.json
βββ src/
β βββ index.ts # MCP Server entrypoint (tool routing)
β βββ config.ts # GCP environment variable configuration
β βββ tools/
β βββ search.ts # Search tool implementations (Data Plane)
β βββ admin.ts # Admin tool implementations (Control Plane)
β βββ billing.ts # Billing & license management tools (Control Plane)
βββ skills/ # Agent Skills templates conforming to Vercel Agent Skills standard
βββ admin-assistant/
β βββ SKILL.md # Skill for DevOps and infrastructure orchestration
βββ enterprise-context/
β βββ SKILL.md # Skill for secure zero-trust enterprise search
βββ codebase-rag/
βββ SKILL.md # Skill for debugging and conversational code contextπ License
This project is licensed under the Apache License, Version 2.0. See the LICENSE file for more information.
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