How to Use the Crystal Matcher MCP in LangChain

Q: Is it possible to use the details tool with Crystal Matcher via LangChain?

Yes, you run getcrystaldetails at the end of your chain. This allows your agent to pull comprehensive specs for a crystal type identified earlier in the process.

Chain together complex logic with LangChain for precise crystal recommendations.

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Connect Crystal Matcher MCP to LangChain

Create your Vinkius account to connect Crystal Matcher 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 Crystal Matcher with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build multi-step reasoning chains.

The `query_crystals_by_intent` tool kicks off the process, letting your agent narrow down crystals based on a user's emotional goal. Then, you can chain that result immediately into `find_crystals_by_chakra`, ensuring the recommendations align with specific energetic points.

Deeply filter results by physical properties.

After getting an initial list of options, run `filter_crystals_by_element` to cut down the choices based on elements like earth or water. This gives your agent a much tighter focus before it even looks at specific details.

Get full specs for final recommendations.

When the chain is almost done, use `get_crystal_details` to pull up all the facts on a single type. This provides necessary context—like crystal size or recommended usage—that your agent needs before passing the final answer to the user.

Setup guide

Set up Crystal Matcher 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 Crystal Matcher 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({
    "crystal-matcher-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 Crystal Matcher transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Crystal Matcher MCP in LangChain

You build a chain where one tool's output feeds directly into the next. For example, your agent runs `query_crystals_by_intent` first, and then uses that resulting crystal list to run `filter_crystals_by_element`. It’s all about sequential logic.

Absolutely. You can first use the agent to identify crystals via their chakra, then immediately pass that list into a call for elemental filtering. The chain handles passing the data cleanly from one step to the next.

This MCP touches crystal catalog data, including element names and chakra alignments. Your agent can read this data to make decisions in a multi-step process.

Yes, you run `get_crystal_details` at the end of your chain. This allows your agent to pull comprehensive specs for a crystal type identified earlier in the process.

It lets you map out complicated logic, like first querying by intent, and then asking for specific chakra matches. The entire sequence of calls remains transparent through the LangChain framework.

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