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Ableton Producer MCP

Ableton Producer MCP

by kovtun-oleksii
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

set_dataset_consent

Record the user's explicit consent response to a dataset question, capturing their answer verbatim only after they have replied in their own words.

Instructions

Record the user's answer to the dataset consent question.

Call this ONLY after the user has answered in their own words. Never infer the answer, never call it on their behalf, and never call it because contributing seems helpful — consent the user did not give is not consent. If they have not been asked yet, ask first and wait for their reply.

Parameters:

  • consent: True if the user agreed to contribute, False if they declined

  • user_said: The user's reply, quoted as closely as possible

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": {
      "consent": "<consent>"
    },
    "name": "set_dataset_consent"
  }
}

Input Schema

NameRequiredDescriptionDefault
consentYes
user_saidNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.4.5+automation.1

TDQS

A4.7/5.0
Behavior4/5

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

Since no annotations are provided, the description carries the full burden. It discloses the critical behavioral constraint that consent must be explicit and given by the user, emphasizing the ethical implications. It doesn't mention what happens after recording (e.g., side effects), but for a simple consent-recording action, the constraints are well disclosed. The description adds value beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is concise and well-structured. It opens with a clear purpose, then provides critical usage rules in a tight paragraph, followed by a straightforward parameter list. Every sentence earns its place with no redundancy or fluff.

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

Completeness4/5

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

The description covers the essential usage and parameters thoroughly. While it doesn't explain the return value or post-consent behavior, the tool has an output schema (per context signals) that would provide that information. Given the tool's simplicity and the schema's presence, the description is complete enough for an agent to call it correctly.

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

Parameters5/5

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

Schema description coverage is 0%, so the description fully compensates. It explains both parameters: consent as a boolean indicating agreement, and user_said as the verbatim reply. This provides clear meaning that the schema alone lacks, making correct parameter usage obvious.

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 states a specific verb ('record') and resource ('the user's answer to the dataset consent question'), making the purpose unambiguous. It clearly differentiates from all sibling tools, none of which handle consent. The scope is precise and leaves no room for confusion.

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

Usage Guidelines5/5

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

The description provides explicit conditions for use: 'Call this ONLY after the user has answered in their own words.' It also states negative conditions ('never infer', 'never call it on their behalf') and instructs to ask first if not yet asked. This is model guidance for when and when not to invoke the tool.

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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