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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct role: generate_sound creates new sounds, mutate_sound creates variations of existing ones, load_sound inspects existing sounds, list_parameters provides reference data, and play_sound is explicitly marked as not to be called. No two tools overlap in purpose.

    Naming Consistency5/5

    All tools follow the same verb_noun snake_case pattern (play_sound, generate_sound, mutate_sound, load_sound, list_parameters), making the naming perfectly consistent and predictable.

    Tool Count5/5

    5 tools is well-scoped for a sound synthesis server. Each tool covers a distinct part of the workflow (create, vary, inspect, reference, and playback), and none feel redundant or unnecessary.

    Completeness5/5

    The toolset covers the core domain of generating and manipulating retro sound effects: generation, mutation, loading existing sounds, and parameter reference. The only potential gap (playback) is explicitly handled by the external library UI, and play_sound is provided as a fallback, so there are no dead ends.

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

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

    • No community issues in the last 6 months
    • 2 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
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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

  • Behavior3/5

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

    No annotations are present, so the description must carry the transparency burden. It discloses the core decoding behavior and the optional .wav side-effect ('optionally re-rendering it to a .wav'), but it does not explain error behavior, file system side effects, or return structure beyond 'parameters'.

    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, front-loaded with the primary purpose, and contains no redundancy. Every word adds value.

    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 tool with 4 parameters and no output schema, the description covers the main action and when to use it, while 'Decode ... into its parameters' implies the return value. It lacks edge-case details like defaults or error handling, but is sufficient for basic invocation.

    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 75%. The description reinforces source semantics by naming 'bfxr.net permalink' and '.bfxr file' and connects render_wav to 're-rendering it to a .wav'. However, it does not add detail about output_dir or name, so it provides only modest context 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 uses the specific verb 'Decode' and identifies the exact resources (bfxr.net permalink or .bfxr file) and outputs ('into its parameters', optionally a .wav). This clearly distinguishes it from sibling tools like generate_sound or play_sound.

    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 provides a clear use case: 'Use this to inspect or edit a sound the user shares.' This tells when to invoke the tool. However, it does not explicitly state when not to use it or name alternatives, so it falls short of a 5.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden. It discloses the random mutation behavior, accepted source forms, and the post-write refresh step. However, it does not explain filesystem side effects, output locations, or whether existing files are overwritten, leaving operational details unclear.

    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?

    Five focused sentences, front-loaded with the core mechanism, then use case, source guidance, constraints, and a follow-up action. Every sentence earns its place with no redundancy.

    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 10-parameter tool with nested objects, no output schema, and no annotations, the description covers most decision-relevant context: what it does, when to use it, source formats, prohibitions, and a post-action step. It omits semantics for save_bfxr/output_dir and exact write behavior, but is otherwise quite complete.

    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?

    Schema coverage is high (80%), so the schema documents most parameters. The description adds valuable context for the source parameter (permallink, .wav/.bfxr paths, raw params) and reinforces constraints on play/embed_audio. It does not compensate for the undocumented save_bfxr and output_dir parameters.

    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?

    Clearly identifies the operation ('nudge every parameter by a small random amount'), target ('existing sound'), and result ('close variations'). This distinguishes it from sibling generate_sound and matches the title/familiar 'Mutate' button reference.

    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?

    Explicitly states when to use ('when the user likes a sound but wants it a bit different') and provides clear exclusions ('never set play or embed_audio, never call play_sound'). It does not explicitly name sibling tools as alternatives, but the contrast is implied.

    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?

    There are no annotations, so the description carries the burden. It discloses that the tool plays via machine speakers and should be used only on explicit request, implying it is an exceptional, potentially disruptive action. However, it doesn't detail other behavioral traits like duration or volume.

    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 three short sentences, front-loaded with a critical warning, and every sentence carries meaningful information. No wasted words.

    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 simple one-parameter tool with a complete schema and no output schema, the description adequately covers purpose, usage context, and the exception. It lacks explicit mention of audio disruption, but 'machine speakers' implies it, making it reasonably complete.

    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?

    The schema provides 100% parameter description ('Path to the .wav file to play.'). The tool description adds no additional parameter semantics, 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 clearly states the tool plays a sound file through machine speakers, with a specific verb ('play') and resource ('sound file'). It distinguishes from the normal web app playback, though it doesn't explicitly name sibling tools.

    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 when-to-use and when-not-to-use guidance: 'Do not call this' normally, and 'Only use if they explicitly ask you to play through the machine speakers.' This clearly differentiates from the standard library web app playback scenario.

    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 transparency burden. It clearly discloses the tool's behavior by enumerating the exact informational content returned: every parameter with range, default, description, plus presets and waveforms. While it doesn't explicitly say 'read-only', listing is inherently non-mutating and the tool has no parameters, so this is adequately transparent for a reference tool.

    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 long and perfectly front-loaded: the first sentence states exactly what the tool provides, and the second gives a clear instruction on when to use it. Every word earns its place, with no redundancy or fluff.

    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's simplicity (0 parameters, no output schema, no nested objects), the description is complete. It explains what is returned (parameter metadata, presets, waveforms) and when to use it, and the sibling tools are all action-oriented, so this reference tool is well-contextualized within the set.

    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 useful semantic context by mentioning `params` as the hand-crafting mechanism in sibling tools, but it doesn't need to describe any input schema since none exists. This is appropriate for a parameterless reference tool.

    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 ('list'), names the resource ('Bfxr parameters and presets'), and explicitly details what is returned (range, default, meaning, presets, waveforms). This clearly distinguishes it from sibling tools like play_sound or generate_sound, which perform actions rather than provide reference data.

    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?

    The description gives an explicit trigger scenario: 'Read this before hand-crafting a sound with `params`.' This tells the agent when to use the tool, but it doesn't explicitly mention when not to use it or name alternative reference tools. Still, for a simple listing tool, this provides sufficient 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?

    With no annotations, the description discloses side effects: saving .wav files, optionally writing .bfxr project files, and requiring a library refresh. It also informs the agent about the bfxr.net permalink in the output and warns against setting play/embed_audio. This gives a clear picture of the tool's behavior, though it doesn't cover file-overwrite or directory-creation edge cases.

    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 structured with clear sections: purpose, preset mapping, shaping params, and usage rules. Every sentence conveys actionable information—no fluff or repetition. Despite its length, it remains efficient and scannable.

    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's 10 parameters, nested params object, and no output schema, the description covers all essential aspects: how to choose presets, how to shape sounds, what not to do, and the follow-up refresh step. It also points to `list_parameters` for detailed ranges, filling any residual 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 input schema already has 100% parameter coverage, so the description adds extra semantic value by grouping useful shaping params with everyday meanings (frequency_start=pitch, sustainTime+decayTime=length, lpFilterCutoff=muffle). It also maps presets to user intents, which helps select the right preset. This goes beyond the schema's basic descriptions.

    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 'Synthesize a retro/8-bit game sound effect with the Bfxr engine and save it as a .wav file.' This clearly states the action (synthesize), the resource (retro/8-bit game sound effect), and the output (.wav file). It also distinguishes from siblings by explicitly saying 'never call play_sound'.

    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 provides preset mapping rules (coin→pickup_coin, laser→laser_shoot, etc.), and tells the agent to use `tone` or `random` for anything else. It also gives explicit exclusions ('Never set play or embed_audio, and never call play_sound') and practical guidance ('Prefer count=3..5', 'refresh the library in Cursor'). These instructions define exactly when and how to use this tool.

    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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