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Dalejan

Volleyball MCP Server

by Dalejan
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.

    Naming Consistency5/5

    A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'execute_query' follows a clear verb_noun pattern.

    Tool Count2/5

    One tool is too few for a server named 'Volleyball MCP Server', which suggests a domain that would typically require multiple operations (e.g., querying players, matches, statistics). A single SQL execution tool feels thin and under-scoped for this apparent purpose.

    Completeness1/5

    The tool surface is severely incomplete for a volleyball domain. It only provides raw SQL execution, lacking any domain-specific operations like retrieving player stats, match results, or team information, which would be expected for such a server.

  • Average 3.1/5 across 1 of 1 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

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  • 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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool executes SQL queries and returns results as a list of tuples, which covers basic behavior. However, it fails to disclose critical traits like whether it's read-only or destructive, authentication requirements, error handling, or rate limits. This is a significant gap for a database query tool.

    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 concise and well-structured, using clear sections for Args and Returns. Each sentence serves a purpose: stating the tool's function, describing the parameter, and explaining the return value. There is no unnecessary information, making it efficient for an agent to parse.

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

    Completeness3/5

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

    Given the tool's complexity (executing SQL queries) and lack of annotations or output schema, the description is minimally adequate. It covers the basic purpose, parameter, and return format, but misses important contextual details like safety warnings, error conditions, or database-specific constraints. Without an output schema, more detail on return values would be beneficial.

    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 description adds meaningful context for the single parameter 'query' by specifying it as 'La query SQL a ejecutar' (The SQL query to execute), which clarifies its purpose beyond the schema's basic type information. Since schema description coverage is 0%, the description compensates adequately, though it could provide more details on query format or restrictions. With only one parameter, the baseline is high.

    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's purpose: 'Ejecuta una query SQL en la base de datos de voleibol' (Executes an SQL query in the volleyball database). It specifies the verb (execute) and resource (SQL query on volleyball database), making the function unambiguous. However, without sibling tools, there's no opportunity to distinguish from alternatives, preventing a perfect score.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives. It lacks information about prerequisites, such as required permissions or database connection details, and does not mention any constraints or best practices for SQL queries. This leaves the agent without context for appropriate usage.

    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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