ffmpeg__extract_frames_for_vision

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

With no annotations provided, the description carries the full behavioral burden. It discloses that output is JSON on stdout, includes frame_paths and frame_count, and explains the output directory fallback chain (FFMPEG_OUTPUT_DIR or SHELL_AS_MCP_OUTPUT_DIR). This provides useful behavioral context, though it doesn't mention side effects like writing files or potential resource usage.

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 front-loaded with a clear one-sentence purpose, followed by a concise @remarks line and structured @param entries. Each line serves a purpose and the whole document is easy to scan, with no fluff.

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?

Given the tool's complexity (8 parameters, no output schema), the description does an excellent job of covering return format, parameter semantics, fallback directories, and the overall purpose. It is nearly complete, though it doesn't specify the output image format or discuss edge cases like empty frame extraction, keeping it slightly short of a perfect score.

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?

The input schema has no descriptions (0% coverage), but the description includes a @param line for every one of the 8 parameters, with meaningful details such as an example for fps and an explanation for keyframes_only. This fully compensates for the schema gap and adds significant semantic value.

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 starts with 'Extract image frames for multimodal vision prompts,' clearly stating the verb and resource. It mentions returning JSON with frame_paths and frame_count, but does not explicitly differentiate from sibling tools like generate_thumbnail or process_video_for_llm, so it is clear but lacks sibling differentiation.

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 multimodal vision prompts but provides no explicit guidance on when to choose this tool over alternatives, nor any exclusions or prerequisites. It does not mention when to use this instead of generate_thumbnail or process_video_for_llm.

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