ffmpeg__process_video_for_llm

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

No annotations are provided, so the description carries the full burden of disclosure. It does reveal that the tool returns a compact JSON summary in stdout, which is useful contextual information. However, it does not mention whether the output file will be overwritten, permission requirements, or failure modes, leaving some behavioral gaps for a write operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a concise opening summary followed by a systematic @param list. While it is somewhat long due to the number of parameters, each line serves a purpose and the front-loaded purpose statement is effective.

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

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

For a 10-parameter video processing tool with no output schema, the description provides a clear output contract (JSON summary), documents all parameters with examples, and explains the combined operation. It lacks detail on error handling or edge cases, but overall it is sufficiently complete for an agent to invoke correctly.

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

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

Schema description coverage is 0%, but the description includes @param lines for all 10 parameters with clear explanations and examples (e.g., 'max_resolution Optional longest-edge cap (for example 720)'). This fully compensates for the schema's lack of descriptions and adds significant meaning beyond raw types.

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

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

The description clearly states the tool's purpose: 'Build an LLM-ready video in one ffmpeg run' and enumerates specific operations (trim, scale, fps, speed, optional audio strip, watermark). This distinguishes it from sibling tools like add_fade or extract_frames, which have narrower scopes.

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

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

The description implies usage for preparing videos for LLM consumption and mentions optional operations, but does not explicitly state when to prefer this over using separate ffmpeg tools or alternatives. No exclusions or alternative tool references are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.