ai_musicGenerate a full song with vocals (Suno V4.5), 2 track variants + cover art. ~$0.15. Takes 1-3 minutes.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Genre, mood, topic — lyrics auto-written | |
| instrumental | No | true = no vocals |
| 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 annotations (which only indicate non-read-only and open-world interaction), the description discloses meaningful operational detail: cost (~$0.15), runtime (1-3 minutes), model version (Suno V4.5), and output specifics (2 track variants + cover art). This gives the agent essential expectations for invocation and side effects.
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, front-loaded sentence begins with the core action and includes key constraints (cost, time) and outputs. Every word contributes value; 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?
The description covers critical operational aspects (cost, duration, output details) and benefits from an output schema for return values. It does not mention prerequisites or async behavior, but for a generation tool with such clear scope, it is nearly 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?
Schema description coverage is 100%, with both 'prompt' and 'instrumental' already documented in the input schema. The tool description does not add any parameter-specific meaning beyond what the schema provides, so baseline 3 is appropriate.
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?
Description states a specific verb 'Generate' targeting 'full song with vocals', with concrete deliverables ('2 track variants + cover art') and model (Suno V4.5). This clearly distinguishes it from sibling tools like ai_image, ai_video, and ai_voice, making its purpose unmistakable.
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 use for music generation but does not explicitly mention when to use it versus alternatives (e.g., ai_voice) or state exclusions. No contrasting guidance is provided, so usage context is only implicit rather than explicit.
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.