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analyze_video

Analyze any video to extract a structured context bundle for coding agents, enabling them to fix bugs, add features, or build new functionality from recorded demos, walkthroughs, or bug captures.

Instructions

Analyze any video and return the new report id.

Works on any kind of video — a bug recording, a feature demo, a design walkthrough, a Loom, a phone capture — and distills it into a structured Context Bundle a coding agent can act on (fix a bug, add or change a feature, or build something new).

Args: path: Path to the video file (.mp4/.webm/.mkv/.mov/.avi). repo_root: Repo to ground references against (pass the open workspace). intent: The user's request to act on, e.g. "fix the save button that hangs", "add a dark-mode toggle like the demo shows", or "build this onboarding screen from the walkthrough". It is recorded on the report and shapes the generated action prompt so the calling agent does what the user actually asked. skill: Built-in summary style — one of the names from list_skills (e.g. "summary", "bug_report", "tutorial", "action_items"). Defaults to "summary". system_prompt: A fully custom system prompt for the summary; overrides skill when provided. action: Built-in action mode shaping the fix-prompt — one of the names from list_actions (e.g. "fix", "explain", "triage", "test", "report", "reproduce"). Auto-picked from the classification when omitted. action_prompt: A fully custom action task; overrides action. ctx: The MCP request context, injected by the server; used to stream progress to the client during the (multi-minute) analysis.

Returns the report id, the summary/fix-prompt resource URIs, the resolved action, and the derived suggested_actions menu.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
skillNo
actionNo
intentNo
repo_rootNo
action_promptNo
system_promptNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Beyond the annotations, the description discloses important behavioral traits: analysis takes multiple minutes, progress is streamed via ctx, action is auto-picked when omitted, and system_prompt/action_prompt override skill/action. This adds meaningful context without contradicting the annotations.

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 long but every section earns its place: a one-line summary, use-case framing, a complete Args block, and Returns expectations. It is well-structured and front-loaded with the core purpose.

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?

The description is self-contained: it covers purpose, usage, all parameter semantics, behavioral notes, and return values. It references sibling tools like list_skills and list_actions for valid enum values, and the output schema is complemented by the explicit Returns list.

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

Parameters5/5

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

Since schema description coverage is 0%, the description fully compensates by explaining every parameter in detail: path formats, repo_root grounding, intent examples and effect, skill/action enum hints with defaults, and override relationships. This goes far beyond the raw schema types and null defaults.

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 opens with 'Analyze any video and return the new report id', which clearly states the verb, resource, and output. It distinguishes itself from sibling get_*/list_* tools by emphasizing creation of a new report from video input, reinforced by the diverse video examples.

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?

The description provides clear when-to-use context: any video that needs to be distilled into an actionable Context Bundle for a coding agent. It does not explicitly mention alternatives or exclusions, but the use case is specific enough for an agent to select it appropriately.

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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