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Python
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video_analysis

Ask questions about one or more videos and get text answers. Describe scenes, analyze shots, transcribe dialogue, or assess pacing from local files or video URLs, with parallel processing for batch requests.

Instructions

Watch one or more videos (local file paths or video URLs) and answer a question about each — returns text, not new video. Use to read a clip before generating or matching it, to describe what happens in it, or to transcribe what is said. Pass requests to watch several videos in ONE call — they are analyzed in parallel, so a batch costs about the same wall time as its slowest clip. Analyzes a clip inline, so a very large file may be rejected — trim or link a shorter clip if so. Set dry_run=true to preview the request without spending.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
videoNoThe video to analyze — a local file path or an http(s) video URL.
promptNoThe question to answer about this video — e.g. 'Describe the shots, the camera moves, the pacing, and transcribe what is said.' Omit for a general description.
dry_runNoIf true, return the request that would be sent (key and video masked), make no API call.
requestsNoAnalyze several videos in one call (1-10). Each entry takes its own `video` and optional `prompt`. Use this instead of one call per video whenever you have more than one to watch — they run in parallel. Supply either `requests` or a single `video`, not both.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changedv0.1.21
    • changedInput schema / properties / prompt / description
      Previous value: -"The question to answer about the video — e.g. 'What happens, and what camera movement, pacing, and audio does it use?' Omit for a general breakdown."New value: +"The question to answer about this video — e.g. 'Describe the shots, the camera moves, the pacing, and transcribe what is said.' Omit for a general description."
    • addedInput schema / properties / requests
      Added value: +{
+  "description": "Analyze several videos in one call (1-10). Each entry takes its own `video` and optional `prompt`. Use this instead of one call per video whenever you have more than one to watch — they run in parallel. Supply either `requests` or a single `video`, not both.",
+  "items": {
+    "additionalProperties": false,
+    "properties": {
+      "prompt": {
+        "description": "The question to answer about this video — e.g. 'Describe the shots, the camera moves, the pacing, and transcribe what is said.' Omit for a general description.",
+        "type": "string"
+      },
+      "video": {
+        "description": "The video to analyze — a local file path or an http(s) video URL.",
+        "type": "string"
+      }
+    },
+    "required": [
+      "video"
+    ],
+    "type": "object"
+  },
+  "maxItems": 10,
+  "minItems": 1,
+  "type": "array"
+}
    • changedInput schema / required
      Previous value: -[
-  "video"
-]New value: +[]
  2. First observedv0.1.13

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description carries full burden. It discloses the key behavioral traits: output is text (not video), parallel execution for batches, potential rejection of very large files due to inline analysis, and dry_run for previewing without spending. It also implicitly notes cost by referencing 'spending' in the dry_run context. No contradictions and substantial transparency.

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?

The description is front-loaded with the core purpose, then delivers use cases, batch advice, a caveat, and dry_run in a logical flow. Every sentence carries necessary information without redundancy. It is packed but well-organized, striking a good balance between detail and brevity.

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

Completeness5/5

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

For a tool with no output schema and no annotations, the description adequately covers what an agent needs to call it: it names the input types, explains the output type ('text'), covers the batch scenario, flags the large-file risk, and explains dry_run. Missing details like error handling or exact response format are not critical given the simplicity of the output promise.

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%, so all parameters are already documented. The description adds practical meaning beyond the schema: for 'requests' it explains parallel run and the mutual exclusion with 'video', for 'prompt' it gives an example and the default behavior if omitted, and for 'dry_run' it clarifies what the preview returns. This adds genuine value, though the baseline of 3 is raised slightly.

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 states a specific verb ('Watch') and resource ('one or more videos') and clearly defines its output ('returns text'). It distinguishes from siblings like video_generate by explicitly saying 'not new video', and from image_analysis by the video domain. It also mentions transcription, which overlaps with the transcribe sibling but clarifies it happens as part of video analysis.

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

Usage Guidelines5/5

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

The description provides explicit use cases: 'Use to read a clip before generating or matching it, to describe what happens in it, or to transcribe what is said.' It also gives guidance on batching via the 'requests' parameter and explains when to prefer it. It mentions inline analysis and the large-file caveat, and describes dry_run for previewing. Clear situational guidance.

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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