How to Use the BattleMetrics MCP in LangChain

Q: Can LangChain agents search for specific BattleMetrics game servers?

Yes. The agent uses the searchservers tool to apply granular filters like country, minimum player count, and rank range. It handles pagination automatically by passing the pagenumber argument if results exceed the initial limit.

Q: What happens if my LangChain agent needs BattleMetrics ban details?

The model calls listbans to find the target record. Since it needs specific permissions, ensure your API token has the ban:read scope attached. It then passes the ban ID to getban for the full context.

Build ReAct agents in LangChain that query BattleMetrics stats, audit player sessions, and trace ban histories automatically.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect BattleMetrics MCP to LangChain

Create your Vinkius account to connect BattleMetrics 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.

GDPR Free for Subscribers

Setup BattleMetrics with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain BattleMetrics MCP Server Queries

Your LangChain agent needs to investigate a suspicious player. It starts by calling `list_players` with a username to grab the exact BattleMetrics identifier. The agent evaluates the output, extracts the ID, and immediately feeds it into `get_player` to pull their linked Steam and EOS accounts. That output then flows directly into `get_player_sessions`. The reasoning pipeline checks the timestamps against server logs to verify exactly when the user joined. You get full observability in LangSmith, showing exactly which tools fired and how long the API took to respond.

Analyze Population Trends

Pulling raw server metrics is just the first step. You can build a chain that triggers `search_servers` to find top-ranked servers matching specific game criteria. The agent grabs the returned server IDs and maps them to the `get_server_player_count_history` tool. Passing ISO 8601 timestamps lets the model fetch historical data across peak hours. Your agent processes those arrays to output a final summary of population drops or spikes over the weekend. Every step executes sequentially without manual intervention.

Automate Organization Ban Audits

Managing moderation across multiple servers gets messy fast. Hook up the MCP Server's `list_bans` tool to your LangGraph pipeline to pull recent enforcement actions. The agent iterates through the paginated results, identifying organization-wide scopes versus server-level restrictions. When it finds a vague entry, the chain triggers `get_ban` to fetch the specific administrator ID and the exact reason logged. You can wire this data into a Slack alert node, creating a fully automated daily report of who caught a ban and why.

Setup guide

Set up BattleMetrics 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 BattleMetrics 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({
    "battlemetrics-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 BattleMetrics transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by BattleMetrics. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect BattleMetrics now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about BattleMetrics MCP in LangChain

Install the langchain-mcp-adapters package first. Then initialize a MultiServerMCPClient pointing to your Vinkius endpoint URL. Call client.get_tools() and pass the array directly into your ReAct agent constructor.

Yes. The agent uses the search_servers tool to apply granular filters like country, minimum player count, and rank range. It handles pagination automatically by passing the page_number argument if results exceed the initial limit.

The model calls list_bans to find the target record. Since it needs specific permissions, ensure your API token has the ban:read scope attached. It then passes the ban ID to get_ban for the full context.

Not necessarily. LangChain handles the sequence of tool executions internally. If you want persistent context across multiple chat turns, use client.session() to keep the connection alive.

Your agent pulls sensitive player IP addresses and linked Steam IDs directly into memory. We run this MCP Server inside a strict V8 Isolate Sandbox that destroys itself after execution. No logs remain on our infrastructure once the connection closes.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript