Skip to main content
Glama
FiloHany

Video RAG MCP Server

by FiloHany
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
RAGIE_API_KEYYesYour Ragie API authentication key

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

NameDescription
ingest_data_toolB
Loads data from a directory into the Ragie index. Wait until the data is fully ingested before continuing.

Args:
    directory (str): The directory to load data from.

Returns:
    str: A message indicating that the data was loaded successfully.
retrieve_data_toolB
Retrieves data from the Ragie index based on the query. The data is returned as a list of dictionaries, each containing the following keys:
- text: The text of the retrieved chunk
- document_name: The name of the document the chunk belongs to
- start_time: The start time of the chunk
- end_time: The end time of the chunk

Args:
    query (str): The query to retrieve data from the Ragie index.

Returns:
    list[dict]: The retrieved data.
show_video_toolB
Creates and saves a video chunk based on the document name, start time, and end time of the chunk.
Returns a message indicating that the video chunk was created successfully.

Args:
    document_name (str): The name of the document the chunk belongs to
    start_time (float): The start time of the chunk
    end_time (float): The end time of the chunk

Returns:
    str: A message indicating that the video chunk was created successfully

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.5/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: ingest_data_tool loads data into an index, retrieve_data_tool queries the index for information, and show_video_tool generates video chunks from retrieved metadata. The descriptions clearly differentiate their functions, making misselection unlikely.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with snake_case naming: ingest_data_tool, retrieve_data_tool, and show_video_tool. This predictable naming convention makes the tool set easy to understand and navigate.

Tool Count3/5

With only 3 tools, the set feels thin for a video RAG system. While the core operations (ingest, retrieve, show) are covered, typical RAG workflows might benefit from additional tools like index management, query refinement, or batch processing. The count is borderline but functional.

Completeness4/5

The tools cover the essential RAG lifecycle: ingestion, retrieval, and video generation. However, there are minor gaps such as missing update/delete operations for the index, query history, or configuration tools. Agents can work around these, but the surface is not fully comprehensive.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources