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mcp-ffmpeg

mcp-ffmpeg

by priyanshum143
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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start_subtitle_format_change

Convert subtitle format in a video file to a specified target format. Returns job ID for tracking.

Instructions

Enqueue a CHANGE_SUBTITLE_FORMAT job. Converts the subtitle format according to the user's provided details. Returns immediately with job_id + status.

input_file: path to input video file (must exist) target_format: target subtitle format force_run: if True, run even when a cached result exists for the same inputs

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": {
      "input_file": "<input_file>",
      "target_format": "<target_format>"
    },
    "name": "start_subtitle_format_change"
  }
}

Input Schema

NameRequiredDescriptionDefault
input_fileYes
target_formatYes
force_runNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.6

TDQS

A4.1/5.0
Behavior4/5

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

Since annotations are missing, the description carries full burden. It discloses the async behavior (returns immediately with job_id+status) and the force_run option for caching. However, it doesn't mention error handling or required permissions.

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 concise and front-loaded with the core action and return behavior. It lists parameters with brief explanations without unnecessary fluff. Every sentence adds value.

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?

Given the output schema exists (though not shown), the description adequately explains the async return with job_id+status. Sibling tools for checking job results and status provide complete context for the workflow.

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 description coverage is 0%, so the description adds meaningful context for parameters: 'path to input video file (must exist)' and 'target subtitle format'. The force_run parameter is also explained. This compensates for the lack of schema descriptions.

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 clearly states it enqueues a job to convert subtitle format, using a specific verb and resource. This distinguishes it from sibling tools like start_audio_extraction or start_change_format.

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

Usage Guidelines2/5

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

No guidance is given on when to use this tool versus alternatives, such as start_change_format or other conversion tools. No conditions or prerequisites are mentioned.

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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