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MuseScore MCP Server

MuseScore MCP Server

by ghchen99
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Add Label

add_label

Places a colored boxed staff text label at the start of a chosen measure, such as a section tag or fill cue, and saves it in the MuseScore score for later removal with clear_highlights.

Instructions

Add a label in a colored box (a boxed staff text) at the start of a measure, e.g. a section tag like 'Groove x7' or 'Fill'. Saved in the score; remove with clear_highlights.

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": {
      "measure": "<measure>",
      "text": "<text>"
    },
    "name": "add_label"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesLabel text
staffNoStaff to attach the label to (0-based)
borderNoBox border color as "#rrggbb"#000000
measureYesMeasure number (1-based)
backgroundNoBox color as "#rrggbb" (default pale yellow)#fff2a8

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses persistence ('Saved in the score') and the removal path (clear_highlights), but says nothing about whether the label is anchored to the measure start only, what happens on repeated calls, or what the call returns.

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?

Two compact clauses: the core action with illustrative examples front-loaded, then the persistence/removal note. No filler, and the most decision-relevant information comes first.

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?

For a five-parameter mutation tool with no output schema and no annotations, the description covers the essential 'what' and the lifecycle hook. It leaves minor gaps around return value and duplicate-label behavior, which are low stakes here.

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 coverage is 100%, so every parameter is already documented, including the '#rrggbb' color format and the default pale yellow. The description only reinforces the box/color concept and the measure-start placement, adding no format or constraint detail beyond the schema.

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

Purpose4/5

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

States a specific verb and resource — adds a boxed staff text label at the start of a measure — and clarifies the concept with concrete examples ('Groove x7', 'Fill'). The phrase 'boxed staff text' implicitly distinguishes it from plain text tools, though it never names a sibling such as add_text or add_rehearsal_mark.

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?

Usage is only implied: the examples show section-tag style labels, and the closing clause points to clear_highlights for removal. There is no explicit statement of when to prefer this over add_text, add_rehearsal_mark, or label_section_counts, so the agent must infer the boundary from the examples.

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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