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levea-mcp-server

autonomous_edit_streaming

Same as autonomous_edit but consumes the SSE event stream from the backend. Returns the final terminal result; per-step events (status, thinking, tool_call, tool_result, background_job_completed) are emitted as MCP progress notifications when the client supports them. Use for long-running briefs where users expect live progress.

Input Schema

NameRequiredDescriptionDefault
sceneNoOptional existing scene/timeline state to continue editing (advanced).
assetsNoOptional asset descriptors. Each item: { id?, type: "video"|"image"|"audio", url|src, mimeType?, duration? }.
promptYesNatural-language description of the desired edit or creative brief. The backend planner decomposes it into canonical actions (viral clips, captions, reframe, export, …). Send the user's intent verbatim — do not pre-decompose into multiple calls.
brandIdNoOptional brand-profile id to apply for this call (palette, fonts, logo, voice, gradeBias). Look up ids via list_brand_kits. Overrides project default brand.
core_onlyNoIf true, response carries only rendering-essential scene fields (no debug metadata).
video_urlNoOptional source video URL. Auto-seeded as the starting layer when no scene is provided.
editedPlanNoOptional pre-edited plan object returned by a prior awaiting_approval response.
project_idNoOptional project id for tracking / continuity.
flaggedIssuesNoOptional list of specific user-flagged problems for this run (e.g. ["caption #3 misspelled"]).
workingMemoryNoWorking-memory snapshot returned by a prior call (typically one that returned awaiting_approval). Re-sending resumes that run.
attachedImagesNoOptional base64-encoded reference images the agent should consider while planning.
projectBrandIdNoOptional default brand for the project. Persists; per-call brandId overrides.
captionTemplateModeNoHow to apply captionTemplatePreset.
requirePlanApprovalNoIf true, the agent stops after planning and returns { status: "awaiting_approval", workingMemory, plan } without mutating. Resume by re-calling with the returned workingMemory + an approval prompt ("yes", "approve", "do it").
captionTemplatePresetNoOptional caption template slug to apply when (re)generating captions. One of 41 builtins (hormozi, mrbeast, viral-pop, minimal-pro, karaoke, neon-pop, typewriter, pop-wave, subtitle-bar, creator-box, …) or a user-saved template id from list_caption_templates.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries real weight: it discloses that only the final terminal result is returned, that per-step events arrive as MCP progress notifications, and critically that this only happens when the client supports them. That conditional is exactly the kind of behavioral caveat an agent needs before choosing this over the plain sibling. It doesn't address mutation scope or the approval/resume loop (left to the schema).

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 tight sentences: differentiation first, return/progress behavior second, usage guidance last. Nothing is repeated from the schema and no sentence is filler.

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 complex 15-parameter mutation tool with no annotations and no output schema, the description covers the key unknowns an agent faces: what comes back (terminal result only), how progress is surfaced, and its client-support dependency. The approval/resume flow and scene-mutation effects are only in the parameter descriptions, so a little context is missing at the top level.

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 100% across all 15 parameters, including long explanations for prompt, requirePlanApproval, workingMemory, and editedPlan, so the schema does the heavy lifting. The description itself adds no parameter semantics beyond what the schema already states. Baseline 3 applies.

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

Purpose4/5

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

The description defines the tool by explicit contrast with a named sibling ('Same as autonomous_edit but consumes the SSE event stream'), so the agent knows this is the streaming variant of the editing tool and what differs. It never states the editing verb/resource directly, relying on the reader knowing autonomous_edit, which keeps it short of a self-contained 5.

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?

'Use for long-running briefs where users expect live progress' gives a concrete selection condition, and the 'same as X but...' framing implicitly routes everything else to autonomous_edit. It stops short of naming the alternative and exclusion explicitly, so it's a strong 4 rather than a 5.

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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