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MukundaKatta

sql-mcp

by MukundaKatta
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no ambiguity. The tool's purpose is clearly defined.

    Naming Consistency5/5

    With a single tool, naming consistency is trivially maintained. The name 'format' is a clear verb indicating its action.

    Tool Count2/5

    A single tool for a SQL server is far too sparse. Users would expect multiple tools for querying, explaining, or managing databases.

    Completeness1/5

    The server only offers formatting, missing essential SQL operations like executing queries or analyzing schema, making it severely incomplete.

  • Average 4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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 is failing

  • 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?

    The description adds behavioral context by mentioning the default keyword uppercasing and the number of supported dialects. However, it does not disclose error handling, authentication requirements, or behavior with invalid input. With no annotations, the description carries the full burden, and while it adds some value, it lacks depth on potential side effects.

    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 concise (two sentences) and front-loaded with the action. Every sentence provides necessary information: the primary function, the range of dialects, and a key default. There is no unnecessary text.

    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?

    The tool is simple with no output schema and no siblings. The description covers the core functionality, supported dialects, and default behavior. It lacks details on return format and error handling, but for a formatting tool these are minor gaps. Overall, it provides sufficient context for an AI agent to understand and invoke the tool correctly.

    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 description coverage is low (25% for only the 'uppercase' parameter). The description adds context for the 'uppercase' parameter by explaining the default behavior, and it clarifies the 'dialect' parameter by enumerating 14 dialects. However, it does not explain the 'sql' or 'tab_width' parameters, so the description only partially compensates for the low schema coverage.

    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 formats SQL queries, specifies the supported dialects, and mentions the default behavior of uppercasing keywords. It uses a specific verb ('Format') and resource ('SQL query'), 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 Guidelines4/5

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

    The description implies when to use the tool (when formatting SQL) and lists supported dialects, but does not explicitly state when not to use it or provide exclusion criteria. Since no sibling tools exist, the guidance is adequate.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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