How to Use the TVMaze MCP in LangChain

Q: How do I find a person's full career history using TVMaze and LangChain?

You first run searchpeople to get the person's ID. Then, you pass that ID into getpersoncastcredits. This function compiles every show name, character name, and episode count for that individual.

Q: Can LangChain use TVMaze to check a specific show’s cast?

Absolutely. You use the getshow tool to confirm the show's details first. After getting the ID, you call getshowcast. This pulls all current and past actors associated with that particular title.

Q: Does TVMaze support finding future schedules using LangChain?

Yes. The client uses the getfullschedule tool to get a comprehensive list of known future episodes. You can optionally narrow this large dataset by specifying a country code.

Q: What kind of data does TVMaze provide regarding show seasons?

The getshowseasons tool provides the season number, name, premiere date, and network. It gives you enough info to build an episode guide for any given series.

Q: If I search for a single show name with TVMaze and LangChain, what details do I get?

The singlesearch tool is best here. It returns exactly one result or none, but it embeds massive amounts of detail—including episode guides, cast info, and network data—all in one go.

Build multi-step reasoning pipelines for LangChain using the TVMaze MCP Server.

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Connect TVMaze MCP to LangChain

Create your Vinkius account to connect TVMaze 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 TVMaze with LangChain

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Perplexity

Chaining Data with the MCP Server

You can construct complex workflows where one tool's result becomes the next tool's input. For instance, your agent first runs `search_shows` to find a show's ID. It then uses that ID immediately in `get_show_cast` to pull the full roster of actors. The resulting cast list can then be passed into another step, like calling `get_person_cast_credits`, letting your agent map out every single role for those actors across different titles.

Observing Complex Tool Calls in LangChain

The framework lets you track exactly how the AI client reaches its conclusion. It shows the latency and inputs for every function call, like checking `get_show` details followed by fetching `get_show_crew`. This observability means you know precisely which tool did what. If you need to check a person's history, your agent can first run `search_people` and then follow up with `get_person` using the numeric ID found in the initial search results.

Automated Schedule Analysis for LangChain

Never manually piece together schedules again. Your client runs `get_full_schedule`, grabbing all future episodes across multiple networks. You can then refine that massive dump by asking it to filter the results using a country code, like 'US' or 'GB'. Alternatively, if you only care about one show, running `get_show_episodes` gives you every season and episode summary for that specific title.

Setup guide

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

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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 LangChain

You first run `search_people` to get the person's ID. Then, you pass that ID into `get_person_cast_credits`. This function compiles every show name, character name, and episode count for that individual.

Absolutely. You use the `get_show` tool to confirm the show's details first. After getting the ID, you call `get_show_cast`. This pulls all current and past actors associated with that particular title.

Yes. The client uses the `get_full_schedule` tool to get a comprehensive list of known future episodes. You can optionally narrow this large dataset by specifying a country code.

The `get_show_seasons` tool provides the season number, name, premiere date, and network. It gives you enough info to build an episode guide for any given series.

The `single_search` tool is best here. It returns exactly one result or none, but it embeds massive amounts of detail—including episode guides, cast info, and network data—all in one go.

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