How to Use the An API of Ice And Fire MCP in LlamaIndex

Build a searchable knowledge base in LlamaIndex using live data from the Ice and Fire universe.

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Connect An API of Ice And Fire MCP to LlamaIndex

Create your Vinkius account to connect An API of Ice And Fire 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 An API of Ice And Fire with LlamaIndex

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Index An API of Ice And Fire data in LlamaIndex

Turn API responses into vector embeddings automatically. By running `list_characters` and indexing the output, you create a searchable index that your agent queries for semantic matches. You avoid hallucinations because the agent grounds its answers in the indexed data. It's a better way to handle deep lore questions.

Query live facts with your MCP Server

Call `get_house` to grab the latest information on noble houses. LlamaIndex then integrates these facts into your RAG pipeline as a source of truth. Your agent pulls from a mix of your static documents and this dynamic API data. It's built for applications that require current information alongside existing knowledge.

Automate RAG pipelines with custom tools

Use `list_books` to define the scope of your knowledge base. The framework handles the tool execution and transforms the results into queryable nodes. You maintain control over which tools are allowed in your index. This ensures your agent focuses only on the specific parts of the series you care about.

Setup guide

Set up An API of Ice And Fire 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 An API of Ice And Fire 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 An API of Ice And Fire tools.",
)
response = await agent.run("List recent An API of Ice And Fire data")

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Common questions about An API of Ice And Fire MCP in LlamaIndex

Install llama-index-tools-mcp and initialize the BasicMCPClient. You then convert the server tools into a tool spec for your agent.

Yes, by indexing the tool outputs into a vector store. The agent retrieves relevant facts from the index instead of guessing about the books.

LlamaIndex handles the semantic search part after it retrieves data from the API. The server provides the raw facts, and the framework makes them searchable.

The server doesn't enforce strict limits, but you should filter your requests using the available tool parameters to keep your index relevant.

The server only touches public series data like character names and house regions. Your infrastructure remains locked down, and the API data is fetched over encrypted channels.

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