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gabguerin

Hooktheory MCP Server

by gabguerin
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.2.3

  • Disambiguation5/5

    The two tools have clearly distinct purposes: get_chord_transitions retrieves chord statistics and transition probabilities, while get_songs_by_progression finds songs containing specific chord progressions. There is no overlap in functionality, making it easy for an agent to select the correct tool based on whether it needs analytical data or song references.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern with get_ as the verb prefix and descriptive nouns (chord_transitions, songs_by_progression). The naming is predictable and readable, adhering to snake_case throughout without any deviations or mixed conventions.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for a music theory domain, as it lacks essential operations like searching for chords, analyzing melodies, or accessing other Hooktheory features. This minimal set may force agents to work around gaps, limiting the server's utility beyond basic queries.

    Completeness2/5

    The tool surface is severely incomplete for a Hooktheory server, missing core functionalities such as chord lookup, melody analysis, or accessing user data. While the existing tools cover chord transitions and song searches, they do not provide a full CRUD/lifecycle or comprehensive coverage of the music theory domain, leading to potential agent failures in broader tasks.

  • Average 4.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 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.

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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?

    With no annotations provided, the description carries the full burden. It discloses that the tool returns paginated results (~20 per page) and a JSON string, which adds useful behavioral context beyond the basic read operation. However, it lacks details on rate limits, error handling, or authentication needs.

    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 well-structured and front-loaded with the purpose, followed by clear sections for arguments and returns. Every sentence adds value without redundancy, making it efficient and easy to parse.

    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 tool's moderate complexity (4 parameters, 1 required) and the presence of an output schema (which covers return values), the description is largely complete. It explains parameters thoroughly and mentions pagination, though it could benefit from more behavioral context like error cases or limitations.

    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?

    The description adds significant meaning beyond the input schema, which has 0% description coverage. It explains each parameter's purpose with examples (e.g., 'cp' uses chord IDs like '1,5,6,4', 'page' defaults to 1, 'key' and 'mode' as filters), fully compensating for the schema's lack of documentation.

    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 specific action ('Get songs that contain a specific chord progression') and resource ('from Hooktheory'), distinguishing it from the sibling tool 'get_chord_transitions' which likely focuses on chord transitions rather than songs by progression.

    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 usage by specifying the required 'cp' parameter and optional filters, but does not explicitly state when to use this tool versus the sibling 'get_chord_transitions' or other alternatives, leaving some ambiguity.

    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 provided, the description carries full burden and does well by explaining the tool's behavior: it describes what happens with and without the 'cp' parameter, specifies the return format (JSON string with specific fields), and provides concrete examples of output probabilities. However, it doesn't mention rate limits, authentication requirements, or potential errors.

    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 well-structured and efficiently organized. It starts with the core purpose, then explains parameters with examples, then describes returns with concrete scenarios. Every sentence adds value, and the information is appropriately front-loaded with the most important details first.

    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 moderate complexity, no annotations, and the presence of an output schema, the description provides excellent contextual completeness. It explains the tool's purpose, parameters, behavior, and return format with concrete examples. The output schema handles return value details, allowing the description to focus on behavioral context.

    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?

    The description adds substantial value beyond the schema's 0% coverage. It explains all three parameters: 'cp' (chord progression with examples and default behavior), 'key' (musical key filter), and 'mode' (scale mode filter). It provides concrete examples and clarifies the impact of each parameter on the tool's behavior.

    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: 'Get chord statistics and transition probabilities from Hooktheory database.' It specifies both overall statistics and transition analysis, distinguishing it from the sibling tool 'get_songs_by_progression' which likely returns songs rather than statistical 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 provides clear usage context by explaining when to use the tool with or without the 'cp' parameter. It distinguishes between overall chord frequencies and transition probabilities, but doesn't explicitly mention when to use this tool versus the sibling 'get_songs_by_progression' or other 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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  • Evaluate tool definition quality.

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