Running ADK agents with MCP tools from a REST API

Google's Agent Development Kit (ADK) is a code-first framework: you write a Python module, declare an Agent, give it tools, and run it. That is great when one developer owns one agent. It gets harder when a team needs dozens of agents, changes their instructions every week, and wants to know who ran what, on which model, for how many tokens.

apowerb is an open source (Apache-2.0) platform we built for that second case. Agents are stored as database records, created from a web UI or a REST API, and apowerb builds the ADK objects for you. This post walks through creating an ADK agent with an MCP server attached, using nothing but HTTP calls, and shows what came back on a real run.

From a record to an ADK agent

Each agent is a row: a name, an instruction, a model string, a list of tools, optional sub-agents and MCP servers. A function called to_agent() turns that row into an ADK object:

llm agents become an LlmAgent; sequential, parallel and loop agents become a SequentialAgent, ParallelAgent or LoopAgent, with their sub-agents built recursively.
The model string is wrapped in ADK's LiteLlm model, so any LiteLLM provider works. Gemini models take the gemini/ prefix.
Each entry in mcp_servers becomes an ADK McpToolset.

When an agent changes, apowerb regenerates its ADK module and the ADK FastAPI app mounted inside apowerb picks it up. Runs go through the standard ADK runner, so sessions, events and traces are plain ADK.

1. Get a token

import requests BASE_URL = "http://localhost:8000" EMAIL = "you@example.com" response = requests.post( f"{BASE_URL}/api/auth/token", data={"username": EMAIL, "password": "YOUR_PASSWORD"}, ) response.raise_for_status() HEADERS = {"Authorization": f"Bearer {response.json()['access_token']}"}

2. Create an agent with an MCP server

For the test I used DeepWiki's public MCP server, which needs no account and answers questions about GitHub repositories.

agent = requests.post( f"{BASE_URL}/api/agents", headers=HEADERS, json={ "agent_name": "docs_assistant", "agent_description": "Answers questions with the tools of an MCP server", "agent_instruction": "You are a helpful assistant. Use the MCP tools when they fit the request.", "agent_model": "gemini/gemini-flash-latest", "agent_model_params": {"model_api_key": "YOUR_GEMINI_API_KEY"}, "agent_type": "llm", "agent_tools": [], "mcp_servers": [ {"name": "my_mcp_server", "transport": "http", "url": "https://mcp.deepwiki.com/mcp"} ], }, ) agent.raise_for_status()

Remote servers use the http transport by default. The stdio transport launches a local command, so it is off unless the operator sets MCP_STDIO_ENABLED=1, and only administrators can use it.

3. Run it

result = requests.post( f"{BASE_URL}/api/adk/run", headers=HEADERS, json={ "agent_name": "docs_assistant", "user_id": EMAIL, "session_id": "session_001", "new_message": { "role": "user", "parts": [{"text": "Use your tools: what is the GitHub repository google/adk-python about?"}], }, }, ) events = result.json()

The response is the list of ADK events for the turn. On the real run, the events contained a functionCall to the MCP tool ask_wiki_question, followed by the model's answer:

The google/adk-python repository contains the Agent Development Kit (ADK), an open-source, code-first Python framework designed for building, evaluating, and deploying sophisticated AI agents.

Asked "Which tools do you have access to?", the same agent listed the three DeepWiki tools (ask_wiki_question, read_wiki_structure, read_wiki_contents) next to apowerb's own built-in tools.

The same body posted to /api/adk/run_sse streams the events as server-sent events. The full conversation and its execution trace are then available at /api/adk/sessions/{agent}/{user}/{session} and .../trace.

What the platform adds around ADK

ADK stays in charge of the agent loop. apowerb adds what a team needs around it:

a web UI and REST API to create and edit agents without redeploying code;
built-in tools for RAG, Text-to-SQL, BI charts, Google Workspace and Microsoft 365;
scheduled runs and runs triggered by email;
per-user token quotas and a per-session audit trail;
self-hosting with Docker Compose or a Helm chart.