How to Use the TVMaze MCP in LlamaIndex

Q: How do I track a person's roles using TVMaze and LlamaIndex?

Use getpersoncastcredits to fetch all credits for an actor. By indexing this data, you build a searchable record of their entire career history, which is far more useful than just seeing a list.

Q: Can LlamaIndex help me find show images?

Yes. You can use getshowimages to grab metadata about all available pictures for a series. Indexing these URLs and types lets you build an asset management knowledge base.

Q: If I search for shows with TVMaze and LlamaIndex, what data is stored?

The searchshows tool returns results including names, genres, networks, and summaries. When indexed by LlamaIndex, these details form highly specific, retrievable facts within your knowledge graph.

Q: Does TVMaze support getting episode details via LlamaIndex?

The getepisode tool provides the name, summary, air date, and runtime for a single episode. This specific data point is perfect to index as an 'Episode Fact' in your knowledge base.

Q: What happens if I run multiple searches with TVMaze and LlamaIndex?

Every tool output—whether it’s from getshow or getperson—is combined into a single, unified index. This means your application can answer complex questions using data points gathered from multiple API calls.

Build searchable knowledge bases with LlamaIndex using the TVMaze MCP Server.

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Connect TVMaze MCP to LlamaIndex

Create your Vinkius account to connect TVMaze 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 TVMaze with LlamaIndex

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Indexing API Results in LlamaIndex

The power here is that when your agent calls a tool, the result gets indexed into your vector store. You can run `get_show` to gather all genres and runtime data for a title, then index those facts. Later, you query your knowledge base about 'shows over 45 minutes' without running the API again. The same applies if you pull a character bio using `get_person`. That detailed text is saved as searchable context.

Querying Schedule Data with LlamaIndex

You can run `get_schedule` for a specific date and country. Instead of just displaying the list, you index it under 'Scheduled Events Q3 2024'. This means your RAG application can answer questions like, 'What shows are airing on US networks in September?' based purely on that indexed data. If you run `get_full_schedule`, that massive dataset also becomes part of your searchable knowledge base.

Combining Show and Crew Data for LlamaIndex

Need to know who directed a show? Run `get_show` first, then use the ID in `get_show_crew`. By indexing both results together, you create a structured knowledge document. You can then query it later: 'Who were the directors of shows rated 8/10?' This allows you to combine data points that live in different tools into one unified answer.

Setup guide

Set up TVMaze 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 TVMaze 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 TVMaze tools.",
)
response = await agent.run("List recent TVMaze data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by TVMaze. 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.

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Common questions about TVMaze MCP in LlamaIndex

Use `get_person_cast_credits` to fetch all credits for an actor. By indexing this data, you build a searchable record of their entire career history, which is far more useful than just seeing a list.

Yes. You can use `get_show_images` to grab metadata about all available pictures for a series. Indexing these URLs and types lets you build an asset management knowledge base.

The `search_shows` tool returns results including names, genres, networks, and summaries. When indexed by LlamaIndex, these details form highly specific, retrievable facts within your knowledge graph.

The `get_episode` tool provides the name, summary, air date, and runtime for a single episode. This specific data point is perfect to index as an 'Episode Fact' in your knowledge base.

Every tool output—whether it’s from `get_show` or `get_person`—is combined into a single, unified index. This means your application can answer complex questions using data points gathered from multiple API calls.

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