How to Use the Crystal Matcher MCP in LlamaIndex

Q: How does LlamaIndex use Crystal Matcher for enhanced search?

Your agent runs a tool like querycrystalsbyintent, and the resulting crystal list gets stored in your knowledge index. This means you can query past answers or configurations later on.

Q: Can I combine Chakra and Element filtering using Crystal Matcher with LlamaIndex?

Sure thing. Run findcrystalsbychakra to get data, then use that result as context for a subsequent call to filtercrystalsbyelement. Both pieces of information are added to the index.

Q: Does LlamaIndex support persistent use of Crystal Matcher details?

Yes. You can run getcrystaldetails to pull data, and then ensure that output gets indexed. This makes the specific facts about any crystal searchable forever in your application.

Q: What happens if my query is vague with Crystal Matcher using LlamaIndex?

The agent still runs the tool call (e.g., querycrystalsbyintent), and even a general result gets indexed. This ensures that nothing falls into a data gap, giving you something to search against later.

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Connect Crystal Matcher MCP to LlamaIndex

Create your Vinkius account to connect Crystal Matcher to LlamaIndex 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 LlamaIndex

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Create searchable knowledge bases from crystal results.

When your agent runs `query_crystals_by_intent`, the output isn't just a temporary answer; LlamaIndex indexes that result into your vector store. You can then query past sessions or configurations against this live data.

Filter and augment crystal recommendations.

You can combine multiple tools, starting with `find_crystals_by_chakra` to get a list of options, and then use that result to run `filter_crystals_by_element`. Both tool results become part of the indexable knowledge base.

Ground answers using crystal facts.

Running `get_crystal_details` provides structured data. By indexing this output, your RAG application can answer follow-up questions about specific crystals—like their mineral composition or origins—without hallucinating.

Setup guide

Set up Crystal Matcher MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Crystal Matcher MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Crystal Matcher tools.",
)
response = await agent.run("List recent Crystal Matcher data")

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Common questions about Crystal Matcher MCP in LlamaIndex

Your agent runs a tool like `query_crystals_by_intent`, and the resulting crystal list gets stored in your knowledge index. This means you can query past answers or configurations later on.

Sure thing. Run `find_crystals_by_chakra` to get data, then use that result as context for a subsequent call to `filter_crystals_by_element`. Both pieces of information are added to the index.

The server touches crystal catalog data, specifically element names and chakra alignments. This structured info is perfect for building a robust, queryable knowledge base.

Yes. You can run `get_crystal_details` to pull data, and then ensure that output gets indexed. This makes the specific facts about any crystal searchable forever in your application.

The agent still runs the tool call (e.g., `query_crystals_by_intent`), and even a general result gets indexed. This ensures that nothing falls into a data gap, giving you something to search against later.

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