imsdbMCP
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
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| movie_matcherA | This tool uses the movie title to get all scene summaries. Use to find where the user is in the movie. |
| summary_to_fullB | This tool uses the movie title and scene number to get all full scenes from the beginning to scene n, so the LLM can explain the movie up until scene n. |
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 2 tools
The two tools have mostly distinct outputs: movie_matcher returns scene summaries for a title, while summary_to_full returns full scene text up to a specified scene. They are complementary rather than overlapping, though both operate on movie title and scene data, so an agent may briefly need to reason about which retrieval granularity to use.
Both names use snake_case, but they follow different conventions: movie_matcher is a noun-plus-agent-noun pattern, while summary_to_full is a noun-plus-prepositional phrase. The names are readable but do not form a predictable verb_noun or parallel pattern.
Two tools is thin for a movie-script retrieval server, though each has a clear role in the intended summary-to-full-scene workflow. The surface feels borderline minimal rather than well-scoped.
The core workflow of locating scenes via summaries and then fetching full scenes up to a point is covered. However, there is no movie search or listing operation, so the server may dead-end when the exact title is unknown, and there is no way to retrieve a single specific full scene without fetching from the beginning.