mcp-lyricscom
Server Quality Checklist
Latest release: v1.4.2
- Disambiguation5/5
Each tool targets a distinct operation: search_lyrics finds songs by lyric fragment, search_songs finds by title, and get_lyrics retrieves the full lyrics for a specific song. There is no overlap between the search modes and the retrieval step, making tool selection unambiguous.
Naming Consistency5/5All tools follow a consistent verb_noun pattern: search_lyrics, search_songs, get_lyrics. The naming clearly indicates the action and the object, with no mixed conventions or vague verbs.
Tool Count5/5Three tools are appropriate for a lyrics lookup service: two search entry points and one fetcher. This is within the ideal 3-15 range and each tool earns its place without redundancy.
Completeness5/5The tool set covers the complete workflow: find a song by lyrics or title, then fetch the full text. No critical operations are missing; the only minor gap is browsing by artist alone, but the optional artist filter on search_songs mitigates this.
Average 4.6/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 41 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.
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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 readOnly/idempotent/destructive safety, and the description adds valuable behavioral context: the underlying lyrics.com search matches titles and lyrics together, so results are locally ranked/filtered, and match='strict' filters further. This goes beyond annotation-only info.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is slightly long (5 sentences) but every clause earns its place, front-loading purpose and then adding usage and behavioral nuance. No waste, though the final sentence could be split for readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema and strong annotations, the description covers the key functional details: return fields, use cases, and the caveat about title matching. Minor gaps remain (e.g., pagination behavior, potential errors), but the description is adequate for safe invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is moderate (60%) and descriptions for title and match exist. The description adds meaning by explaining artist narrows results and elaborating the effect of match='strict', which compensates for undocumented page/limit/artist and ties parameters to behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Find'), resource ('songs on lyrics.com'), and scope ('by their title'), and clearly distinguishes from siblings by mentioning its role in preparing to call get_lyrics and differentiating covers/versions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says 'Use this when someone names a song...' and gives a second use case (distinguishing covers). It does not explicitly contrast with the sibling search_lyrics, but the guidance is clear enough for selection.
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?
Beyond the annotations (readOnlyHint, idempotentHint), the description adds crucial behavior: truncated responses, the need to check 'truncated' and use 'offset' with 'next_offset', the valid 'no_lyrics' status with empty lyrics, and a citation requirement. This significantly enhances the agent's understanding of the tool's runtime behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four sentences, each earning its place: main purpose, truncation handling, no_lyrics edge case, and citation requirement. It is concise yet information-dense, with no redundant phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the full usage workflow: obtaining the id/url, handling long lyrics through pagination, dealing with legitimate no_lyrics responses, and citing sources. Combined with the provided output schema and sibling context, it is comprehensive for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds meaning by explaining the pagination pattern (offset + next_offset) and tying id/url to sibling search tools, which is useful context beyond the raw schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action: 'Fetch the full lyrics of one song from lyrics.com' given a song id or URL. It clearly distinguishes from sibling search tools by focusing on retrieving a single song's lyrics using identifiers returned by search tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies that input comes from search_lyrics or search_songs, giving clear context for when to use this tool. It also explains the pagination workflow and that no_lyrics is a valid outcome, though it does not explicitly provide when-not-to-use exclusions.
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?
Beyond the readOnly/idempotent annotations, the description reveals that lyrics.com returns loose and title-only matches, so results are locally filtered to keep only genuine matches. It also explains pagination behavior ('One call returns one page... increase page rather than limit') and the impact of verify='none' to see the raw unfiltered list, offering rich behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is five sentences, each serving a purpose: purpose, return content, usage scenario, pagination semantics, and filtering behavior. It is front-loaded with the core action and contains no extraneous words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters and an output schema, the description still provides a complete overview: what is returned, when to use it, how pagination works, how filtering works, and how verification options affect results. It leaves no major questions about invocation or expectations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3. The description adds value by clarifying the functional difference between 'page' and 'limit' and by explicitly describing the effect of verify='none', which is not fully captured by the schema's parameter descriptions alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Search lyrics.com for songs whose lyrics contain a given word or phrase,' which is a specific verb, resource, and scope. It clearly differentiates from siblings by mentioning that the returned id and URL are needed to fetch full text with get_lyrics, positioning search_lyrics as the lookup-by-lyric tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use it: 'Use this when someone remembers a fragment of lyrics but not the song, or wants songs mentioning a word or theme.' It also provides page/limit usage guidance, but does not explicitly name search_songs as the alternative for title-based searches, which would have made it a 5.
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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