ai_video_klingGenerate a 5-second video with Kling 2.6 (no audio). ~$0.60. Takes 1-3 minutes.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Scene description: subject, motion, camera, style | |
| aspect_ratio | No | 16:9 (default) | 9:16 | 1:1 |
| 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?
Beyond the annotations (readOnlyHint: false, openWorldHint: true), the description adds valuable behavioral information: cost (~$0.60) and time (1-3 minutes). These are important for an agent to set expectations and decide whether to invoke the tool, and they do not contradict any annotations.
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 a single sentence with three short pieces of information: purpose, cost, and time. It is front-loaded and every phrase delivers value with 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?
For a relatively simple tool with two parameters and an output schema, the description covers the essential details: model, duration, audio absence, cost, and latency. Combined with the schema’s parameter descriptions and output schema, it is sufficiently complete.
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 100% coverage with descriptive parameter documentation for 'prompt' and 'aspect_ratio'. The description doesn't add additional parameter meaning, so it meets the baseline but doesn't exceed it.
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 function: 'Generate a 5-second video with Kling 2.6 (no audio).' It specifies the exact verb, resource, model, duration, and audio constraint, which distinguishes it from sibling video tools like ai_video and ai_video_seedance.
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 provides clear context for use—5-second videos, no audio, cost, and latency—allowing an agent to infer appropriate scenarios. However, it does not explicitly mention alternatives or when-not-to-use, so it falls short of 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.