ai_video_seedanceGenerate a 5-second 720p video with ByteDance Seedance 2.5 (no audio, fast & cheap). ~$0.35.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Scene description: subject, motion, camera, style | |
| aspect_ratio | No | 16:9 (default) | 9:16 | 1:1 | 4:3 | 3:4 |
| Name | Required | Description | Default |
|---|---|---|---|
| result | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds important behavioral context beyond annotations, including exact duration/resolution, absence of audio, and cost (~$0.35). It is consistent with annotations (not read-only, not destructive) and gives the agent a clear expectation of what the tool produces and at what price.
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?
A single, information-dense sentence includes all key specifications (duration, resolution, model, audio, speed, cost). No filler, and the most important details are front-loaded.
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 straightforward generation tool with an output schema and simple parameters, the description covers all necessary context: what is generated, key specifications, and cost. Nothing essential is missing.
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 already provides helpful descriptions for both parameters (prompt scene description and aspect ratio values). The description itself adds no parameter-specific meaning beyond what the schema offers, so the baseline of 3 applies.
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 generates a video with specific characteristics (5-second, 720p, ByteDance Seedance 2.5), distinguishing it from sibling tools like ai_video and ai_video_kling. The verb 'generate' and resource 'video' are specific and unambiguous.
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 context—'no audio, fast & cheap' suggests it is ideal for quick, low-cost, silent video generation. It provides clear context but does not explicitly name alternatives or when-not-to-use scenarios, which would have earned 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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Four image generation tools, three video tools, and five 'ask' tools create significant overlap. Although descriptions specify the model, an agent must carefully compare prices and capabilities to choose correctly, making misselection likely.
All tool names use snake_case, but patterns are mixed: some start with verbs (remove_bg, scrape_page), some with nouns (crypto_prices, market_snapshot), and many use ai_/ask_ prefixes. Model suffixes like flux, gpt, pro, kling are descriptive but not systematically applied.
24 tools is heavy, inflated by near-duplicate variants for image, video, and LLM queries. While the broad scope justifies a large count, the redundant tools could have been consolidated.
The toolset covers a wide range of media and data tasks: image, video, music, voice, vision, LLM, web, crypto, domain, and endpoint discovery. Notable gaps like speech-to-text or image editing exist, but the surface is fairly complete for a general-purpose media toolkit.