# MCP Servers for Environment Emulation in ADR-Bench: Complete 10-Server Reference > Discover 10 MCP servers for environment emulation in ADR-Bench. Simulate enterprise services like email, Slack, and LDAP securely without exposing real infrastructure. - Repository: [Uber Open Source/ADR](https://github.com/uber/ADR) - Tags: architecture - Published: 2026-08-06 --- **ADR-Bench provides 10 specialized MCP servers for environment emulation, listed in [`Detection/mcp_servers_registry.json`](https://github.com/uber/ADR/blob/main/Detection/mcp_servers_registry.json), which simulate enterprise services like email, databases, Slack, and LDAP without exposing real infrastructure.** ADR-Bench is Uber's framework for benchmarking AI agent behavior against realistic attack scenarios. The **Model-Context-Protocol (MCP) servers** enable safe, sandboxed testing by emulating external services locally. This guide covers every available **MCP server for environment emulation** in the project, drawn directly from the `uber/ADR` source code. ## The MCP Server Registry Architecture All 133 MCP servers in ADR-Bench are catalogued in [`Detection/mcp_servers_registry.json`](https://github.com/uber/ADR/blob/main/Detection/mcp_servers_registry.json). The registry uses a `type` field to classify servers by their execution model. For **environment emulation**, filter by `type: "local_environment"`. These servers spawn as local Python processes, execute in isolated sandboxes, and log all operations for deterministic replay. ```python import json from pathlib import Path # Load the registry from the repository registry_path = Path("Detection/mcp_servers_registry.json") with registry_path.open() as f: data = json.load(f) # Isolate environment-emulating servers env_servers = { name: info for name, info in data["servers"].items() if info.get("type") == "local_environment" } print(f"Found {len(env_servers)} environment emulation servers") ``` ## Complete List of MCP Servers for Environment Emulation ADR-Bench ships with **10 `local_environment` MCP servers** across six functional categories. Each server exposes capabilities via the MCP protocol and runs under `context_providers/source_codes/mcp_servers_2/`. ### Communication Servers (4) **email_server** - **Purpose:** Emulates corporate email infrastructure - **Key capabilities:** `send_email`, `get_contacts`, `search_emails` - **Entry point:** `uv run python email_server.py` **slack_server** - **Purpose:** Emulates Slack workspace operations - **Key capabilities:** `send_message`, `get_channel_history`, `upload_file` - **Entry point:** `uv run python slack_server.py` **sms_server** - **Purpose:** Emulates SMS gateway services - **Key capabilities:** `send_sms`, `get_inbox_messages`, `schedule_sms` - **Entry point:** `uv run python sms_server.py` **http_server** - **Purpose:** Emulates generic HTTP/web request handling - **Key capabilities:** `upload_file`, `send_get_request`, `download_content` - **Entry point:** `uv run python http_server.py` ### Database Servers (1) **database_server** - **Purpose:** Emulates relational database operations - **Key capabilities:** `execute_query`, `backup_table`, `get_schema_info` - **Entry point:** `uv run python database_server.py` ### File System Servers (1) **file_server** - **Purpose:** Emulates full file-system access - **Key capabilities:** `read_file`, `write_file`, `list_directory` - **Entry point:** `uv run python file_server.py` ### Developer Tool Servers (1) **github_server** - **Purpose:** Emulates GitHub repository and secret management - **Key capabilities:** `get_repository`, `list_repository_contents`, `create_issue` - **Entry point:** `uv run python github_server.py` ### AI Service Servers (1) **openai_server** - **Purpose:** Emulates OpenAI API endpoints - **Key capabilities:** `chat_completion`, `list_models`, `create_image` - **Entry point:** `uv run python openai_server.py` ### Security Infrastructure Servers (2) **ldap_server** - **Purpose:** Emulates LDAP/Active Directory authentication - **Key capabilities:** `authenticate_user`, `search_users`, `modify_user` - **Entry point:** `uv run python ldap_server.py` **api_gateway_server** - **Purpose:** Emulates API gateway with key management - **Key capabilities:** `make_api_request`, `create_api_key`, `revoke_api_key` - **Entry point:** `uv run python api_gateway_server.py` ## How MCP Servers Are Loaded in ADR-Bench The **`MCPServerManager`** class in [`Detection/main_benchmark.py`](https://github.com/uber/ADR/blob/main/Detection/main_benchmark.py) orchestrates server lifecycle. It reads [`mcp_servers_registry.json`](https://github.com/uber/ADR/blob/main/mcp_servers_registry.json), constructs configuration objects, and injects server capabilities into benchmark task prompts. ```python from Detection.main_benchmark import MCPServerManager, Config # Initialize benchmark configuration cfg = Config.from_file("Detection/tasks.json") mcp_manager = MCPServerManager(cfg) # Retrieve configuration for specific emulation server slack_cfg = mcp_manager.get_server_config("slack_server") print(f"Command: {slack_cfg.command}") print(f"Args template: {slack_cfg.args_template}") # Manager spawns process when task executes; agent calls via MCP protocol # e.g., `slack_send_message` becomes available in the task context ``` When a benchmark task declares a server dependency, the manager executes the command defined in the registry—typically `uv run python {server_name}.py`—and proxies all MCP protocol traffic through the spawned subprocess. ## Inspecting Server Capabilities Programmatically Capabilities are declared as arrays in the registry. Access them for task planning or validation: ```python def list_capabilities(server_name: str, registry_data: dict) -> list[str]: """Return capability list for a given environment emulation server.""" server = registry_data["servers"].get(server_name, {}) return server.get("capabilities", []) # Example: inspect http_server capabilities caps = list_capabilities("http_server", data) print("http_server provides:") for cap in caps: print(f" - {cap}") # Output: upload_file, send_get_request, download_content ``` ## Key Source Files for MCP Environment Emulation | File | Function | |------|----------| | [`Detection/mcp_servers_registry.json`](https://github.com/uber/ADR/blob/main/Detection/mcp_servers_registry.json) | Master registry of all 133 MCP servers with types, commands, and capability lists | | [`Detection/main_benchmark.py`](https://github.com/uber/ADR/blob/main/Detection/main_benchmark.py) | Implements `MCPServerManager` for registry parsing and process spawning | | [`context_providers/source_codes/mcp_servers_2/email_server.py`](https://github.com/uber/ADR/blob/main/context_providers/source_codes/mcp_servers_2/email_server.py) | Email emulation implementation | | [`context_providers/source_codes/mcp_servers_2/slack_server.py`](https://github.com/uber/ADR/blob/main/context_providers/source_codes/mcp_servers_2/slack_server.py) | Slack workspace emulation implementation | | [`context_providers/source_codes/mcp_servers_2/ldap_server.py`](https://github.com/uber/ADR/blob/main/context_providers/source_codes/mcp_servers_2/ldap_server.py) | LDAP/AD authentication emulation implementation | | [`context_providers/source_codes/mcp_servers_2/api_gateway_server.py`](https://github.com/uber/ADR/blob/main/context_providers/source_codes/mcp_servers_2/api_gateway_server.py) | API gateway emulation implementation | | [`Detection/README.md`](https://github.com/uber/ADR/blob/main/Detection/README.md) | Benchmark setup and server usage instructions | ## Summary - **ADR-Bench provides 10 MCP servers for environment emulation** marked `type: "local_environment"` in the registry - **Categories covered:** Email, Slack, SMS, HTTP, databases, file systems, GitHub, OpenAI, LDAP, and API gateways - **Safe execution model:** All servers run as local sandboxed processes with full operation logging - **Registry location:** [`Detection/mcp_servers_registry.json`](https://github.com/uber/ADR/blob/main/Detection/mcp_servers_registry.json) contains complete server definitions - **Orchestration:** `MCPServerManager` in [`Detection/main_benchmark.py`](https://github.com/uber/ADR/blob/main/Detection/main_benchmark.py) handles dynamic loading ## Frequently Asked Questions ### What file contains the list of all MCP servers for environment emulation? The complete list resides in [`Detection/mcp_servers_registry.json`](https://github.com/uber/ADR/blob/main/Detection/mcp_servers_registry.json) at the repository root. This JSON file defines 133 total MCP servers, with 10 specifically designated as `local_environment` type for sandboxed emulation. ### How does ADR-Bench ensure environment emulation servers don't touch real services? ADR-Bench enforces isolation through **local process execution**. The `MCPServerManager` spawns server implementations from `context_providers/source_codes/mcp_servers_2/` as subprocesses with no network egress. All external API calls are mocked, and every operation is logged to local files for reproducible evaluation. ### Can I add custom MCP servers for environment emulation to ADR-Bench? Yes. Create a new server implementation in `context_providers/source_codes/mcp_servers_2/`, then register it in [`Detection/mcp_servers_registry.json`](https://github.com/uber/ADR/blob/main/Detection/mcp_servers_registry.json) with `type: "local_environment"` and your capability list. The `MCPServerManager` will automatically load it on next benchmark run. ### What is the difference between `local_environment` and other MCP server types in ADR-Bench? `local_environment` servers are **sandboxed, stateful emulations** designed for attack testing without side effects. Other types in the 133-server registry include remote API wrappers, static context providers, and tool adapters—these may contact real external services or provide read-only data without emulation logic.