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?
Annotations are present (readOnlyHint: false, destructiveHint: false), so the agent already knows it is a write operation. The description adds valuable behavioral context: cost ('~$0.15'), latency ('Takes 1-3 minutes'), and output specifics ('2 track variants + cover art'). It also names the model version (Suno V4.5), which helps set expectations. No contradiction with 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 that front-loads the primary action, then adds cost and time as separate concise clauses. Every word contributes value, with no redundancy or filler. It is highly scannable and appropriately sized.
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 low complexity (2 parameters, one required) and the presence of an output schema, the description covers the main operational aspects: what it generates, cost, and duration. It leaves out potential failure modes or usage restrictions, but these are not essential for basic invocation. The completeness is strong for a generation tool, though not exhaustive.
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 100% description coverage for both parameters, so the baseline is 3. The description itself does not add further semantic details about parameters like 'prompt' or 'instrumental'; it only refers to the overall song generation. Schema descriptions already explain the prompt and the 'true = no vocals' meaning for instrumental.
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 uses a specific verb 'Generate' and clearly identifies the resource: 'a full song with vocals (Suno V4.5)'. It also specifies the scope ('2 track variants + cover art') and distinguishes this tool from siblings like ai_image, ai_video, and ai_voice by focusing on music generation.
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 does not provide explicit guidance on when to use this tool versus alternatives. It only states what it does, but there are no exclusions, prerequisites, or references to sibling tools like ai_voice or ai_image. The phrase 'full song' implies music generation, but it never says 'use this for generating songs' directly.
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.