How to Use the Amilia MCP in LangChain

Q: How do I connect the Amilia MCP server to LangChain?

Install langchain-mcp-adapters and define a MultiServerMCPClient pointing to your Vinkius endpoint URL. Pass the result of client.gettools() to your ReAct agent.

Q: Can LangChain agents check Amilia program schedules?

Yes. Your agent calls listactivityoccurrences to pull exact dates and times for any class. The framework automatically parses the JSON response into a format the LLM understands.

Build multi-step recreation management pipelines with Amilia and LangChain.

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Connect Amilia MCP to LangChain

Create your Vinkius account to connect Amilia to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Amilia with LangChain

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Claude

Perplexity

LangChain agents for program discovery

The `list_programs` and `list_activities` tools feed your LangChain agents raw recreation catalogs. Your agent parses summer camp schedules or fall session metadata, deciding which activity matches user criteria before passing the results down the pipeline. Because LangChain handles state between MCP calls, you chain these lookups directly into availability checks. The output from finding a specific gymnastics class becomes the exact input needed for `list_activity_occurrences`, letting the agent map out the whole season without dropping context.

Automate roster and attendance chains

The `get_attendance` tool pulls live participant lists directly into your workflow. ReAct agents check who showed up to a specific session and trigger follow-up actions based on absence patterns. LangSmith tracing tracks every API call your agent makes to the Amilia MCP server. You see exactly how many tokens were spent pulling the attendance data, making it trivial to debug why an agent decided a class was full.

Cross-reference family accounts via MCP Server

You pull family profiles using the `list_accounts` and `list_contacts` tools. Instead of manual data entry, your pipeline fetches the parent details and immediately cross-references them against active enrollments using `list_registrations`. Complex queries resolve in seconds. An agent grabs a user's account ID, finds their registered programs, and outputs a formatted itinerary—all managed through a single LangChain script.

Setup guide

Set up Amilia MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Amilia tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "amilia-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Amilia transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Amilia MCP in LangChain

Install `langchain-mcp-adapters` and define a `MultiServerMCPClient` pointing to your Vinkius endpoint URL. Pass the result of `client.get_tools()` to your ReAct agent.

Yes. Your agent calls `list_activity_occurrences` to pull exact dates and times for any class. The framework automatically parses the JSON response into a format the LLM understands.

You might be missing persistent context between tool calls. Use `client.session()` so the agent remembers the account ID from `list_accounts` before it tries to run `get_account_details`.

Tracing works out of the box. Every tool invocation logs the exact input parameters and the JSON response returned by the server.

We run the server in a V8 Isolate Sandbox that destroys itself after the request finishes. Your API tokens never touch persistent storage, keeping parent names and child attendance rosters isolated from other processes.

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