Name: TV Show Recommender
Author: terryso

Question 1

What is the TV Recommender MCP Server?

Accepted Answer

It's an MCP (Model Context Protocol) server that enhances LLMs with the ability to provide personalized American TV show recommendations by accessing the TMDb API.

Question 2

What is MCP (Model Context Protocol)?

Accepted Answer

MCP is an open protocol standardizing how applications provide context to LLMs, enabling AI models to interact with external data sources and tools.

Question 3

How does the TV Recommender MCP Server work?

Accepted Answer

It connects to the TMDb API via stdio (standard input/output), retrieves TV show information, and uses it to provide recommendations through an LLM client.

Question 4

What are the key features of this server?

Accepted Answer

Key features include genre-based recommendations, similar show suggestions, detailed show information, actor-based recommendations, trending show discovery, and watch provider lookups.

Question 5

What is TMDb?

Accepted Answer

TMDb (The Movie Database) is a community-built movie and TV database. The API provides access to a wealth of information about various TV shows.

Question 6

How do I install the TV Recommender MCP Server?

Accepted Answer

You can install it globally using npm (`npm install -g tv-recommender-mcp-server`) or by cloning the repository and installing the dependencies.

Question 7

What is a TMDb API key, and how do I get one?

Accepted Answer

A TMDb API key is a unique identifier that allows you to access the TMDb API. You can obtain one for free by creating an account on the TMDb website ([https://www.themoviedb.org/](https://www.themoviedb.org/)).

Question 8

How do I configure the TMDb API key?

Accepted Answer

Set the `TMDB_API_KEY` environment variable with your API key.

Question 9

What are the system requirements for running this server?

Accepted Answer

You need Node.js installed on your system.

Question 10

How do I run the server?

Accepted Answer

After installing, run the `tv-recommender-mcp-server` command in your terminal, ensuring the `TMDB_API_KEY` environment variable is set.

Question 11

How do I use this server with an LLM client?

Accepted Answer

Configure your MCP-compatible LLM client (e.g., Claude Desktop, Cursor) to communicate with the server via stdio.

Question 12

How do I use this with Cursor?

Accepted Answer

Configure the `mcp.json` file in the `.cursor` folder of your project.

Question 13

Can I use this server with Smithery?

Accepted Answer

Yes, you can install the service from the Smithery platform ([https://smithery.ai](https://smithery.ai)).

Question 14

What tools does the server provide?

Accepted Answer

The server offers tools for getting recommendations by genre (`get_recommendations_by_genre`), finding similar shows (`get_similar_shows`), getting show details (`get_show_details`), finding available watch providers (`get_watch_providers`), advanced show discovery (`discover_shows`), and more.

Question 15

What is the discover_shows tool?

Accepted Answer

The `discover_shows` tool allows for more specified show searches using parameters like keywords, genre, year of release, or trending status.

Question 16

How can I find shows starring a specific actor?

Accepted Answer

Use the `find_shows_by_actor` tool, providing the actor's name.

Question 17

Can I get recommendations based on a specific actor's work?

Accepted Answer

Yes, use the `get_recommendations_by_actor` tool.

Question 18

How can I find popular or trending shows?

Accepted Answer

Use the `get_popular_shows` and `get_trending_shows` tools.

Question 19

How do I find trailers for a show?

Accepted Answer

Use the `get_show_videos` tool with the show's title.

Question 20

How do I get the reviews of a show?

Accepted Answer

Use the `get_show_reviews` tool and provide the show's title.

Question 21

Is there any rate limiting?

Accepted Answer

Yes, be aware of the TMDb API's rate limits. Implement retry logic or caching if needed.

Question 22

Is the API key secure?

Accepted Answer

The API key is loaded from environment variables and is not hardcoded in the source code. Ensure that the `.env` file is excluded from version control.

Question 23

Is this project open source?

Accepted Answer

Yes, it is licensed under the MIT license.

Question 24

Where can I report issues or contribute to the project?

Accepted Answer

You can submit issues and pull requests on the GitHub repository.

Question 25

How do I contribute to the project?

Accepted Answer

Fork the repository, make your changes, and submit a pull request.

Question 26

What is the recommended workflow for contributing?

Accepted Answer

Create a new branch for your feature or bug fix, and submit a pull request against the `main` branch.

Question 27

Where can I find more detailed documentation?

Accepted Answer

Visit the [DeepWiki documentation](https://deepwiki.com/terryso/tv-recommender-mcp-server) for detailed information about tool usage, architecture, deployment, and more.

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