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reka-ai

Reka Vision MCP Server

Official
by reka-ai

upload_video

Upload a video from a URL to get a video_id, enabling asynchronous indexing and analysis of the video content.

Instructions

Upload a video from a URL. Returns a video_id. Local file paths are not accepted; upload files outside the MCP server and pass a reachable video_url. The upload runs asynchronously — poll get_video until status is 'uploaded', then call index_video to enable search and analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
group_idNo
rationaleNo
video_urlYes
descriptionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.10

TDQS

A4.2/5.0
Behavior4/5

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

With empty annotations, the description carries the full burden and delivers well: it discloses the asynchronous upload, the returned video_id, rejection of local file paths, and the required polling handshake before the video is usable. It stops short of describing failure behavior for unreachable URLs or auth/permission requirements, which are material gaps for a write operation with zero annotation coverage.

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

Conciseness5/5

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

Three sentences, front-loaded with purpose and return value before the constraints and workflow. Every sentence carries operational information — no filler, no tautology, and the critical async caveat appears early.

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

Completeness4/5

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

For a 5-parameter async ingest tool with an output schema present, the description covers the essential operating loop (upload → poll → index) and the key input constraint. It omits failure handling and the intended purpose of the optional parameters, but the workflow guidance is sufficient for an agent to invoke it correctly in the common case.

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

Parameters3/5

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

Schema description coverage is 0%, and the description compensates for the one required parameter (video_url) with explicit semantics: a reachable URL is mandatory and local paths are rejected. The four optional parameters (name, group_id, rationale, description) receive no additional meaning beyond their property names, so compensation is only partial.

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?

States a specific verb and resource ('Upload a video from a URL. Returns a video_id.') and clarifies what upload means relative to the sibling index_video, which is explicitly positioned as the follow-up step that enables search and analysis. An agent can distinguish it from get_video, list_videos, and index_video without opening any schema.

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?

Gives an explicit operational workflow: upload → poll get_video until status is 'uploaded' → call index_video, plus a hard prerequisite that video_url must be reachable and not a local file path. However, it never states when not to use this tool or how to choose among alternatives, so the guidance is contextual rather than exclusionary.

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