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MuseScore MCP Server

MuseScore MCP Server

by ghchen99
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Append Measure

append_measure

Add a specified number of empty measures to the end of a score, extending its length for continued composition.

Instructions

Append measures to the end of the score.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "append_measure"
  }
}

Input Schema

NameRequiredDescriptionDefault
countNoNumber of empty measures to append

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full burden, and it only says measures go at the end. It does not state that this is a mutating operation, whether it is undoable, whether measure numbering shifts, or what the newly appended empty measures contain.

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?

A single short sentence, front-loaded with the verb and scope, with no redundant wording. It is efficient, though it leaves almost no room for the behavioral detail the tool actually needs.

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

Completeness3/5

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

The tool is simple with one fully documented parameter and no nested objects, so the minimal description is nearly sufficient. However, with no annotations and no output schema, it should still say something about the mutation being applied and its side effects on the score.

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% and the single 'count' parameter is fully described there, including its default of 1. The description adds nothing beyond that, so baseline 3 applies.

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?

The description states a specific verb ('append') and resource ('measures') plus the target location ('end of the score'). It implicitly distinguishes itself from the sibling insert_measure by specifying 'end', but never names or contrasts the alternative explicitly.

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

Usage Guidelines2/5

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

There is no when-to-use guidance at all: it does not say to prefer this over insert_measure when the user wants measures at the end, nor give any prerequisites. An agent must infer the choice from the word 'end' alone.

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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