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?
Annotations already indicate a non-read-only, non-destructive operation. The description adds valuable context beyond annotations: the fixed duration (5 seconds), resolution (720p), lack of audio, speed, and price (~$0.35). No contradiction with annotations; this context helps set expectations for cost and latency.
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 sentence that front-loads the core action and includes essential specs and cost. Every word adds value; there is no redundancy or filler.
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?
With an output schema present and only two simple parameters, the description covers the key behavioral and business aspects (what, specs, cost). No need to explain return values since the output schema exists. The tool definition is adequately complete for an agent to use it 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?
Input schema provides 100% coverage with descriptions for both parameters (prompt and aspect_ratio). The tool description adds no additional parameter-level detail, so the baseline of 3 is appropriate; the schema fully carries the semantic load.
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 states a specific verb ('Generate') with a resource ('5-second 720p video'), names the model (ByteDance Seedance 2.5), and clarifies key specs (no audio, fast & cheap). This clearly distinguishes it from sibling video tools like ai_video and ai_video_kling.
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 ('fast & cheap') but does not explicitly state when to choose this tool over alternatives like ai_video_kling, nor does it provide exclusions for when not to use it. The 'no audio' and cost hints provide some guidance, but it falls short of explicit when/when-not instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes (image, music, video, vision, voice, etc.), but some overlap exists: ask_ai vs ask_ai_pro differ only in model strength, and web_search vs research_report both involve search with AI responses. Descriptions help clarify, though an agent could misselect in edge cases.
Tool names follow a mostly consistent snake_case pattern, with many using an 'ai_' prefix for generation tasks. However, name styles vary between verb_noun (call_endpoint, remove_bg) and noun_verb (crypto_prices, domain_info), and ask_ai/ask_ai_pro break the ai_ prefix convention. Minor deviations, but the overall pattern is readable.
At 16 tools, the server is slightly above the ideal 3-15 range but remains well-scoped for a multi-purpose utility server. Each tool has a distinct function, and the count feels manageable rather than overwhelming.
The server covers a broad set of capabilities (AI generation, web search, crypto, domain info), but it lacks lifecycle management for generated assets—there are no list/get/delete operations for previously created media, and the domain appears to be a collection of paid endpoints rather than a cohesive service.