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CineFlow Studio

CineFlow Studio

by MilayaMariyaAi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

cineflow_quick_photo_animate

Turn a still portrait or product photo into a 5-8 second cinematic video clip using motion prompts for camera moves and natural blinking.

Instructions

Animate any still portrait or product photo into a 5-8 second cinematic video clip via LTX 2.5.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "image_path": "<image_path>"
    },
    "name": "cineflow_quick_photo_animate"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptNoMotion description in cinematic English (e.g. 'camera slowly pans, natural blinking').
durationNoClip duration in seconds (5.0 to 8.0).
image_pathYesPath to input photo.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses the generation model (LTX 2.5) and the bounded clip length, but says nothing about cost, runtime, whether the model must be installed locally, where the output file is written, or whether the operation is reversible/re-runnable.

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?

A single front-loaded sentence that identifies the input, the output, the duration bound, and the model with no filler. Nothing in it is redundant or misplaced.

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?

For a three-parameter generation tool with no annotations and no output schema, the definition covers the action and inputs but omits what the call returns or where the resulting clip lands. An agent could invoke it correctly but could not set expectations about the response.

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%, so all three parameters (image_path, prompt, duration) are already documented in the schema, including the 5.0-8.0 range and an example prompt. The description adds no syntax, format, or constraint detail beyond what the schema already states, so the 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 gives a specific verb ('Animate'), a specific input resource ('still portrait or product photo'), and a concrete output ('5-8 second cinematic video clip'), plus the underlying model (LTX 2.5). It does not name a sibling or explain how it differs from the nearby cineflow_quick_reel, so it falls short of a 5.

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

Usage Guidelines3/5

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

Usage is only implied: the phrase 'any still portrait or product photo' scopes the acceptable input type, which tells an agent this is a photo-to-video operation. There is no explicit when-to-use, when-not-to-use, prerequisite (installed model, auth, hardware), or named alternative such as cineflow_quick_reel.

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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