media-mcp

generate_music

Create instrumental music by providing text prompts with weights for genre, instrument, or mood. Adjust tempo, scale, temperature, and duration for customized output.

Instructions

Generate instrumental music from weighted text prompts using Google's Lyria model.

Each prompt has a 'text' describing genre/instrument/mood and a 'weight' for emphasis. The server manages the streaming session internally.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompts": "<prompts>"
    },
    "name": "generate_music"
  }
}

Input Schema

Name	Required	Description	Default
`bpm`	No
`scale`	No
`prompts`	Yes
`temperature`	No
`duration_seconds`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.7Jul 29, 2026

TDQS

B3.3/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

There are no annotations provided, so the description must fully disclose behavioral traits. It mentions the tool generates music and internally manages streaming, but it does not disclose whether the operation is destructive, requires authentication, has rate limits, or what the output format (e.g., audio file URL) is. This is insufficient for an AI agent to understand 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is only two sentences, making it very concise. It front-loads the primary purpose and then adds a clarifying technical detail. However, it could be slightly more structured by separating parameter explanation from behavioral notes.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of five parameters and no output schema, the description is incomplete. It fails to mention what the tool returns (e.g., an audio file URL or stream), and it does not cover the optional parameters. This leaves significant gaps for an AI agent to correctly invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must add meaning to parameters. It clarifies that the 'prompts' array contains objects with 'text' and 'weight' fields, which aids the agent. However, it does not explain other parameters like bpm, scale, temperature, or duration_seconds, leaving them underdocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it generates instrumental music from weighted text prompts using Google's Lyria model, specifying the verb (generate), resource (music), and technology, distinguishing it from sibling tools like generate_image or generate_speech.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains the prompt structure and mentions server streaming, but it does not provide explicit guidance on when to use this tool versus alternatives, nor does it include prerequisites or exclusions. The context signals indicate sibling tools are different modalities, so the usage is partially implied.

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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