Movie Search MCP Server
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Alternatives to Movie Search MCP Server
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Related Servers
- AlicenseNot gradedqualityDmaintenanceA simple MCP server for fetching movie and TV show data from TMDB and OMDB APIs. Provides natural language capability for users to search, compare, and analyze movies through AI assistants.MIT
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- FlicenseAqualityDmaintenanceAn MCP server that wraps the TMDB API, enabling search of movies and TV shows, retrieval of details, trending titles, recommendations, and streaming provider information.8-
- FlicenseNot gradedqualityDmaintenanceA robust MCP server that wraps The Movie Database API, enabling LLMs to search movies, get details, popular movies, and recommendations.-
- FlicenseNot gradedqualityDmaintenanceA server that connects to The Movie Database API, enabling clients to fetch popular movies, now playing movies, movie details, cast information, and search for movies through a Message Control Protocol interface.9-
- AlicenseAqualityFmaintenanceA MCP server for The Movie Database API that enables AI assistants to search and retrieve movie, TV show, and person information.238 npm4MIT
TDQS
Scored across 5 tools
The tools have distinct names suggesting different purposes (details, help, popular, recommend, search), but without descriptions, there's potential ambiguity. For example, 'popular_movies' and 'recommend_movies' might overlap in functionality if recommendations are based on popularity, and 'movie_help' is vague and could be confused with other tools. The naming helps, but descriptions would be needed to fully disambiguate.
The naming follows a consistent pattern with snake_case and a mix of verb_noun (e.g., 'get_movie_details', 'search_movies') and noun_verb (e.g., 'movie_help', 'popular_movies', 'recommend_movies'). There are minor deviations in word order, but overall it's readable and mostly predictable, with no chaotic mixing of styles.
With 5 tools, this is well-scoped for a movie search server. It covers key operations like searching, getting details, finding popular movies, and recommendations, which aligns with typical movie-related functionalities. The count is appropriate, not too thin or heavy for the apparent domain.
The tool set covers basic search and discovery functions (search, details, popular, recommend), but there are notable gaps. For a movie domain, missing operations might include user-specific actions (e.g., rate_movie, add_to_watchlist), filtering or sorting options, or genre-based searches. The surface is functional but incomplete for a full movie experience, likely causing some agent limitations.