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burakdirin

mysqldb-mcp-server

by burakdirin
OverviewSchemaRelated ServersScoreDiscussions(1)
Python
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: connect_database handles database connections, while execute_query handles query execution. There is no overlap or ambiguity between these functions, making it easy for an agent to select the correct tool based on the task.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (connect_database and execute_query), using snake_case throughout. This predictable naming scheme enhances readability and reduces confusion for agents interacting with the server.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for a MySQL database management system. Core operations like creating tables, inserting data, or managing schemas are missing, which limits its utility for typical database workflows. This count is too low for the apparent domain.

    Completeness2/5

    The tool surface is severely incomplete for MySQL database operations. While connecting and executing queries are foundational, there are significant gaps in CRUD operations (e.g., no create, read, update, or delete tools), schema management, or data manipulation, which will likely cause agent failures in real-world scenarios.

  • Average 2.5/5 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
    • Last stable release on
    • 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.

  • 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": [
    "your-github-username"
  ]
}

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure but fails to do so. It doesn't mention whether this is a read-only or destructive operation, authentication requirements, error handling, rate limits, or what the response looks like. For a database query tool with zero annotation coverage, this is a critical gap in transparency.

    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 extremely concise with just three words, front-loaded and free of unnecessary information. Every word ('Execute MySQL queries') directly contributes to the core purpose, making it efficient in structure, though this brevity contributes to gaps in other dimensions.

    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?

    Given the complexity of a database query tool, lack of annotations, no output schema, and 0% schema description coverage, the description is completely inadequate. It fails to address key aspects like behavioral traits, parameter details, return values, or usage context, making it insufficient for effective agent tool invocation.

    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?

    The schema description coverage is 0%, meaning the input schema provides no descriptions for the 'query' parameter. The description 'Execute MySQL queries' adds no meaningful semantics beyond the parameter name—it doesn't explain the expected format, syntax, constraints, or examples for the query. This leaves the parameter entirely undocumented.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Execute MySQL queries' clearly states the verb ('execute') and resource ('MySQL queries'), making the purpose understandable. However, it lacks specificity about what types of queries are supported (e.g., SELECT, INSERT, UPDATE) and doesn't distinguish from the sibling tool 'connect_database', which appears to be a different operation. This makes it vague but not tautological.

    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 the sibling 'connect_database' or other alternatives. It doesn't mention prerequisites (e.g., whether a database connection must be established first), use cases, or exclusions. This leaves the agent with no contextual direction for tool selection.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It states the action but doesn't disclose behavioral traits like whether this establishes a persistent connection, requires authentication, has rate limits, or what happens on failure. For a connection tool with zero annotation coverage, this leaves critical operational details unspecified.

    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 easy to parse and front-loaded with essential information. Every word earns its place by specifying the action and target.

    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 tool's complexity (establishing a database connection), lack of annotations, no output schema, and low schema coverage, the description is insufficient. It doesn't cover what the tool returns, error conditions, or dependencies with the sibling 'execute_query' tool, leaving too many gaps for effective use.

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

    Parameters2/5

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

    Schema description coverage is 0%, and the description adds no parameter information beyond implying a 'database' parameter exists. It doesn't explain what the 'database' parameter represents (e.g., database name, connection string), valid values, or format, failing to compensate for the schema's lack of descriptions.

    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 ('Connect to') and target resource ('a specific MySQL database'), making the purpose immediately understandable. It doesn't explicitly distinguish from the sibling 'execute_query' tool, but the verb 'connect' versus 'execute' implies different operations.

    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 contextual constraints. With a sibling tool 'execute_query' available, there's no indication of whether connection must precede query execution or if they can be used independently.

    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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