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wenjiachengy

MySQL MCP Server

by wenjiachengy
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Python
Hybrid

Server Quality Checklist

50%
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 confusion or overlap between tools, as there are no other tools to compare it against. The tool's purpose is clearly defined and singular.

    Naming Consistency5/5

    Since there is only one tool, it inherently follows a consistent naming pattern with itself. The tool name uses a clear verb_noun format (execute_sql), which is straightforward and appropriate.

    Tool Count2/5

    A single tool for a MySQL server is too minimal for the typical scope, which usually involves multiple operations like querying, inserting, updating, deleting, and managing databases. This server lacks the breadth expected for database interactions.

    Completeness1/5

    The tool surface is severely incomplete; it only provides a generic SQL execution tool without covering essential CRUD operations, schema management, or specific query types. This will likely cause agent failures due to missing functionality for common database tasks.

  • Average 2.9/5 across 1 of 1 tools scored.

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

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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 mentions execution but lacks details on permissions needed, whether it's read-only or mutative, potential side effects, error handling, or response format. This leaves significant gaps in understanding the tool's behavior.

    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 a single, clear sentence with no wasted words, making it highly concise and front-loaded. It efficiently communicates the core purpose without unnecessary elaboration.

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

    Completeness2/5

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

    Given the complexity of SQL execution (potentially mutative, with security and error implications), no annotations, and no output schema, the description is incomplete. It fails to address critical aspects like return values, safety warnings, or usage constraints, leaving the agent under-informed.

    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 input schema has 100% description coverage, with the 'query' parameter documented as 'The SQL query to execute'. The description adds no additional meaning beyond this, such as query syntax examples or constraints. Baseline 3 is appropriate since the schema does the heavy lifting.

    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 action ('Execute') and target resource ('SQL query on the MySQL server'), making the purpose immediately understandable. It doesn't need to distinguish from siblings since there are none, but it could be more specific about what types of queries are supported (e.g., SELECT, INSERT, etc.).

    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, prerequisites, or limitations. It simply states what the tool does without any context about appropriate use cases, making it minimally helpful for decision-making.

    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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