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contextstream

ContextStream MCP Server

Media

media
Read-onlyIdempotent

Index, search, and retrieve video, audio, and image assets with semantic understanding for AI agents to edit and analyze.

Instructions

Media operations for video/audio/image assets. Enables AI agents to index, search, and retrieve media with semantic understanding - solving the "LLM as video editor has no context" problem for tools like Remotion.

Actions:

  • index: Index a local media file or external URL. Triggers ML processing (Whisper transcription, CLIP embeddings, keyframe extraction).

  • status: Check indexing progress for a content_id. Returns transcript_available, keyframe_count, duration.

  • search: Semantic search across indexed media. Returns timestamps, transcript excerpts, keyframe URLs.

  • get_clip: Get clip details for a time range. Supports output_format: remotion (frame-based props), ffmpeg (timecodes), raw.

  • list: List indexed media assets.

  • delete: Remove a media asset from the index.

Example workflow:

  1. media(action="index", file_path="/path/to/video.mp4") → get content_id

  2. media(action="status", content_id="...") → wait for indexed

  3. media(action="search", query="where John explains authentication") → get timestamps

  4. media(action="get_clip", content_id="...", start="1:34", end="2:15", output_format="remotion") → get Remotion props

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesAction to perform
workspace_idNoWorkspace ID (UUID).
project_idNoProject ID (UUID).
target_projectNoTarget child project by folder name or project name
file_pathNoLocal path to media file for indexing
external_urlNoExternal URL to media file for indexing
content_typeNoType of media content. Use video, audio, image, or document; friendly aliases like photos/images and docs/PDFs/slides are accepted.
content_idNoContent ID from index operation
queryNoSemantic search query for media content
content_typesNoFilter search/list to content types: video, audio, image, document. Friendly aliases are accepted.
startNoStart time for clip. Formats: "1:34", "94s", or seconds as string
endNoEnd time for clip. Formats: "2:15", "135s", or seconds as string
output_formatNoOutput format: remotion (frame-based props for Video component), ffmpeg (timecodes), raw (seconds)
fpsNoFrames per second for remotion format (default: 30)
tagsNoTags to associate with media
limitNoMaximum results to return
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description contradicts annotations: readOnlyHint=true is inconsistent with index and delete actions which modify data. The description itself discloses ML processing and output formats, but the contradiction undermines trust.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with action list and workflow example, but slightly verbose. Front-loads core purpose. Could be trimmed without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the high parameter count (16) and no output schema, the description covers main use cases and provides workflow. However, missing details on error handling, rate limits, and authentication. Adequate but not comprehensive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, baseline 3. The description adds meaningful context beyond schema, e.g., explaining output_format options in detail and accepting friendly aliases for content_type. The example workflow illustrates parameter usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly defines the tool as handling media operations (video/audio/image) with specific actions listed. It distinguishes from sibling tools by focusing on semantic understanding for media, solving a specific problem for tools like Remotion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides a detailed example workflow showing the sequence of actions (index, status, search, get_clip). Clearly indicates when to use each action, but does not explicitly state when NOT to use or alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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