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Server Quality Checklist

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  • Latest release: v1.5.1

  • Disambiguation5/5

    Each tool has a distinct purpose: search_tracks finds tracks via metadata queries, get_track retrieves metadata for a specific ID, and get_lyrics fetches lyrics. There is no functional overlap; even search_tracks and get_track differ in input type (query vs. ID) and output scope (list vs. single object).

    Naming Consistency5/5

    All tools follow the snake_case verb_noun pattern consistently: search_tracks, get_lyrics, get_track. The naming is uniform and immediately conveys the action and resource, making it easy to predict tool behavior.

    Tool Count5/5

    Three tools is well-scoped for a lyrics lookup server: search, metadata retrieval, and lyrics fetching. Each tool earns its place and covers the essential workflow without redundancy or bloat.

    Completeness5/5

    The tool set covers the complete lifecycle for LRCLIB: searching, verifying track metadata, and fetching lyrics in plain or synced format with pagination support. No critical gaps exist; even edge cases like instrumental tracks are addressed.

  • Average 4.7/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 42 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • Tools from this server were used 2 times in the last 30 days.

  • This repository includes a glama.json configuration file.

  • This server has been verified by its author.

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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

  • Behavior4/5

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

    Annotations already declare read-only, idempotent, and non-destructive behavior, so the description is not required to restate those. It adds valuable non-obvious traits: results carry metadata only, synced lyrics availability is indicated before fetching, and LRCLIB cannot full-text search lyrics. This goes beyond the structured hints.

    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 several sentences long but each one earns its place: purpose, parameter usage, result interpretation, and a key limitation. It is front-loaded with the core function and remains information-dense without being bloated.

    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?

    With an output schema present, the description does not need to enumerate return fields. It covers the tool's role in the broader workflow, parameter choice strategies, result-field usage for disambiguation, and a significant search limitation, making it complete for a search tool of this complexity.

    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?

    Because schema coverage is 100%, the baseline is 3, but the description elevates it by explaining when to use 'query' versus separate structured fields and how 'album_name' can narrow a search. This guidance is not fully evident from the schema alone, adding practical parameter-selection semantics.

    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 identifies the tool as searching LRCLIB for tracks by title, artist, or album, and explicitly connects it to retrieving the ID needed for get_lyrics. It uses a specific verb and resource, and distinguishes this discovery-oriented tool from its siblings by framing its output as a prerequisite for lyric lookup.

    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 gives concrete when-to-use guidance by explaining the choice between free-text 'query' and structured search fields, and advises how to disambiguate releases using result metadata. It does not explicitly contrast with get_track, but naming get_lyrics as the follow-up step and stating the metadata-only limitation provides solid directional context.

    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?

    Annotations already declare read-only and idempotent, and the description adds that it returns metadata only (no lyrics) and whether plain and time-synced lyrics exist. This enriches behavioral expectations beyond annotations, though it does not mention potential not-found behavior.

    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 two sentences: the first states the operation and output, the second gives usage guidance. It is concise, front-loaded with the verb, and contains no filler.

    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?

    For a one-parameter, read-only lookup with an output schema and strong annotations, the description fully covers what the tool does and when to use it. The sibling context and explicit usage guidance complete the picture without redundancies.

    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 schema description covers the single parameter fully, noting the id is an LRCLIB track id as returned by search_tracks. The tool description reinforces this by saying 'by its id' and 'track id you already hold,' adding use-case context that complements the 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 looks up a single LRCLIB track by its id and returns metadata without lyrics, listing specific fields. It distinguishes from siblings by focusing on a known id and excluding lyrics, making the purpose unambiguous.

    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 explicitly instructs to use this before get_lyrics to confirm the right release or check timed lyrics, implying search_tracks for finding ids and get_lyrics for actual lyrics. This provides clear when-to-use guidance and names alternatives.

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

  • Behavior5/5

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

    Despite readOnlyHint and idempotentHint annotations, the description adds substantial behavioral context: default truncation with 'truncated' and 'next_offset', instrumental status returning no text, and the requirement to cite 'attribution'. No contradiction with annotations.

    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?

    A single paragraph that is front-loaded with purpose, then efficiently covers input methods, format options, truncation behavior, edge cases, and attribution. Every sentence earns its place without fluff or redundancy.

    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?

    Covers the full workflow: how to obtain input (search_tracks), pre-checks (has_synced_lyrics), format selection, pagination/truncation, instrumental edge case, and attribution requirement. Combined with the rich output schema and annotations, the description leaves no critical gaps.

    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?

    Even with 100% schema coverage, the description enriches parameter meaning: explains the 'format' enum values in user terms, clarifies that 'id' is preferred over name fields, and mentions duration_seconds for disambiguation. This adds value beyond the 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 opens with a specific verb and resource: 'Fetch the lyrics of one track from LRCLIB.' It clearly distinguishes from siblings by focusing on lyric content and explicitly references search_tracks as the source of the id, leaving no ambiguity about what this tool does.

    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?

    Provides explicit workflow guidance: use the id from search_tracks or exact artist/title, check 'has_synced_lyrics' first, and do not retry instrumental tracks. This is actionable when-to-use and when-not-to-use guidance that goes beyond generic alternatives.

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

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