Skip to main content
Glama
ALLLLLL703

midi

by ALLLLLL703
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools serve completely different purposes: one is an environment/readiness check, the other is the core transcription operation. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    Both names use lowercase with underscores, but 'check_model' follows a verb_noun pattern while 'audio_to_midi' is a source_to_target pattern. The naming is clear and readable, with only a minor structural inconsistency.

    Tool Count3/5

    With only two tools, the server feels minimal, but the tools directly cover the essential workflow for a focused MIDI transcription service. This is thin but not unreasonable for such a narrow scope.

    Completeness4/5

    The tool set covers the core workflow: check readiness and perform the audio-to-MIDI conversion. Minor gaps exist, such as no model management or configuration options, but the primary task is fully supported.

  • Average 4.3/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 13 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior3/5

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

    With no annotations, the description carries the behavioral disclosure burden. It mentions the output format ('MIDI file') and input constraints, but does not disclose side effects (e.g., file creation/storage), error behavior, or any required authentication/compute limitations. It is not misleading, but it leaves notable behavioral gaps.

    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 a single focused sentence, front-loaded with the key verb and output, and contains no redundant or filler language. Every word contributes to the understanding of the tool.

    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?

    Given the presence of an output schema and the simple zero-parameter input, the description covers the primary scenario well. It could additionally mention supported audio formats or the exact meaning of 'MuScriptor', but these are not critical gaps for basic invocation.

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

    Parameters4/5

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

    There are no structured parameters in the input schema, so the baseline is 4. The description adds essential semantic context by explaining what the tool operates on (a local or public HTTPS audio file), which is not represented in the empty schema.

    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 uses a specific verb ('Transcribe') and clearly identifies the resource ('audio file') and output ('multi-instrument MIDI file'), making the tool's purpose unambiguous. It also naturally distinguishes itself from the sibling tool 'check_model', which is unrelated.

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

    Usage Guidelines4/5

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

    It establishes clear usage context by specifying accepted input types ('local or public HTTPS audio file') and the intended transformation. While it doesn't explicitly state when not to use it or mention alternatives, the context is sufficiently clear for an agent to decide when to invoke it.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

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

    No annotations are provided, so the description carries the behavioral burden. It discloses a key behavioral trait—that model weights are not loaded—which signals a fast, non-side-effecting check. It does not detail error/return behavior, but the presence of an output schema mitigates that gap.

    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 a single, well-structured sentence that conveys the essential purpose and key qualifier without redundancy. Every word earns its place.

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

    Completeness5/5

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

    Given the tool has no parameters and an output schema exists, the description is sufficiently complete. It names the exact entities being checked and the important behavioral constraint (no weight loading), leaving no major contextual gaps.

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

    Parameters4/5

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

    The tool has zero parameters, so the baseline is 4. The description adds contextual meaning about the readiness check itself, though there is no parameter detail to provide beyond the empty schema.

    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 the tool's purpose: to check readiness of MuScriptor and the writable output directory. It uses a specific verb ('check') and names exact resources, and the qualifier 'without loading model weights' distinguishes this from heavier model-related operations.

    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 implies this is a lightweight pre-flight readiness check by mentioning 'without loading model weights,' but it does not explicitly state when to use this tool versus alternatives or what conditions should trigger its use. No exclusions or alternative guidance are provided.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

midi MCP server

Copy to your README.md:

Score Badge

midi MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ALLLLLL703/midi'

If you have feedback or need assistance with the MCP directory API, please join our Discord server