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KeeVor

keevor-mysql-mcp-server

by KeeVor
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Python
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Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clear, distinct purpose: list_tables retrieves metadata about all tables, describe_table zooms into a single table's structure, and execute_sql runs arbitrary queries. No overlaps.

    Naming Consistency5/5

    All tool names follow a consistent snake_case verb_noun pattern (list_tables, describe_table, execute_sql), making them predictable and easy to understand.

    Tool Count4/5

    With only 3 tools, the server covers the minimum needed for MySQL interaction. While a bit sparse, the tools are well-chosen and each one serves a necessary role, making the count appropriate for a focused utility server.

    Completeness3/5

    The tool set covers basic discovery (list, describe) and execution (execute_sql), but lacks specialized tools for common operations like SELECT, INSERT, or database management. The generic execute_sql fills many gaps, so the surface is functional but not comprehensive.

  • Average 3.6/5 across 3 of 3 tools scored. Lowest: 2.3/5.

    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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
}

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • Behavior1/5

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

    No annotations provided. The description fails to disclose critical behavioral traits such as whether SQL statements are read-only or can modify data, what happens on error, or whether results are returned. For a tool executing arbitrary SQL, this is a significant omission.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is very brief, but it sacrifices necessary information for brevity. It front-loads the purpose but lacks structure for parameter details or usage notes. It is under-specified rather than concise.

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

    Completeness1/5

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

    Despite having only one parameter and an output schema, the description fails to provide essential context about SQL execution behavior, return format, or safety considerations. It is incomplete for an operation with significant potential impact.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters1/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, and the description merely restates the parameter name 'sql: 要执行的SQL语句' without adding any semantics about syntax, limitations, or allowed statements. The agent gains no additional meaning beyond the raw 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 'Execute SQL statement' clearly states the tool's verb and resource. It distinguishes from sibling tools 'describe_table' and 'list_tables' which are for schema inspection rather than execution.

    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?

    No guidance on when to use this tool versus alternatives, nor any mention of prerequisites or when not to use it. The description is minimal and leaves usage decisions entirely to the agent.

    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?

    Despite no annotations, the description discloses that the output includes field comments, indexes, and foreign keys, adding behavioral context beyond basic table schema. It implies a read-only operation.

    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 very concise: two lines for purpose and one line for parameter explanation, with no superfluous text. Front-loaded with the main action.

    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 simple single-parameter input and existing output schema, the description adds value by specifying that details include comments, indexes, and foreign keys. Could mention if it is safe/read-only but not essential.

    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 description coverage is 0% but the description compensates with an in-line 'Args' explanation for table_name, clearly stating it is the table name to query. Adequate for a single parameter.

    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 queries detailed table structure including field comments, indexes, and foreign keys. It distinguishes from siblings: list_tables lists tables, execute_sql runs arbitrary SQL, while describe_table provides schema details for a specific table.

    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?

    No explicit when-to-use or when-not-to-use guidance. Usage is implied from context: use when needing table structure details, but no alternatives or exclusions are mentioned.

    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 provided, but description explicitly states it returns table comments, engine, row count, etc. Safe read operation implicitly clear. Lacks mention of any pagination or limits, but acceptable for this simple 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?

    Single sentence front-loads action and key details. No wasted words.

    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?

    With no parameters and output schema present, description sufficiently explains what is returned (table metadata). No missing aspects for this straightforward tool.

    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?

    No parameters exist; schema coverage 100%. Baseline is 4 per rules. Description adds no redundant info.

    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?

    Description clearly states verb 'list' and resource 'all tables in database' with details. Distinguishes from siblings: describe_table focuses on single table, execute_sql runs arbitrary SQL.

    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?

    No explicit when-to-use or when-not-to-use guidance. Usage is implied as 'when you need an overview of all tables', but no comparison with siblings.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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