Indian Movies MCP Agent
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_movie_recommendationsB | Get Indian movie recommendations based on genre, language, or rating preferences |
| search_movieC | Search for a specific Indian movie by title |
| get_random_movieB | Get a random Indian movie recommendation |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
The tools are mostly distinct with clear purposes: get_movie_recommendations for filtered recommendations, get_random_movie for random selection, and search_movie for specific title lookup. However, get_movie_recommendations and get_random_movie could be slightly confused as both provide recommendations, though one is filtered and the other random.
All tool names follow a consistent verb_noun pattern with snake_case (get_movie_recommendations, get_random_movie, search_movie). The naming is predictable and readable throughout the set.
With only 3 tools, the set feels thin for a movie recommendation domain. While it covers basic recommendation and search functions, it lacks operations for browsing genres, languages, or detailed movie information, which might limit agent effectiveness.
The tool surface is significantly incomplete for an Indian movies domain. There are no tools for getting movie details (e.g., plot, cast, ratings), filtering by criteria beyond basic preferences, or managing user interactions (e.g., saving favorites). This will likely cause agent failures in comprehensive movie-related tasks.