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PlanetMarc

SqlDb-MCP-Server

by PlanetMarc
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Hybrid

Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one executes arbitrary SQL queries, the other retrieves schema metadata. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern with snake_case (execute_query, get_schema_info), making them predictable and uniform.

    Tool Count3/5

    With only two tools, the server feels minimal for a database-oriented MCP. While each tool is useful, the set is borderline thin for the apparent scope.

    Completeness4/5

    The execute_query tool can handle all SQL operations (including CRUD and DDL), and get_schema_info provides structural insight, covering core database needs. Minor gaps like dedicated transaction or table management tools exist but are not critical since they can be performed via SQL.

  • Average 3.6/5 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
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

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  • 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

  • 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 for behavioral disclosure. It merely says 'execute a SQL query' without warning that this could modify data, require specific permissions, or produce side effects. The agent is not informed whether read-only queries are enforced or if destructive operations are possible, which is a critical omission.

    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, front-loaded sentence that wastes no words. It is concise and immediately understandable, earning full marks for conciseness and structure.

    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?

    For a tool that executes arbitrary SQL, the description is severely incomplete. With no output schema and no annotations, the agent has no idea what the return value looks like (e.g., result set, affected row count), what errors might occur, or what safety constraints apply. The minimal description is insufficient for an agent to use this tool effectively and safely.

    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 coverage is 100%, with the 'query' parameter described as 'SQL query to execute'. The description adds no additional meaning beyond the schema, so the baseline score of 3 applies.

    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 executes a SQL query on the database, using a specific verb and resource. This distinguishes it from the sibling tool get_schema_info, which retrieves schema information, so there is no ambiguity about what this tool does.

    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 gives no guidance on when to use this tool versus get_schema_info. It does not mention contexts, prerequisites, or exclusions, leaving the agent to infer appropriate usage without any supporting direction.

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

  • Behavior3/5

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

    With no annotations, the description carries the transparency burden. The term 'Retrieve' implies a non-destructive read, but it does not state permissions, performance characteristics, or the exact return format. This is acceptable for a simple read tool but leaves some ambiguity.

    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?

    A single, front-loaded sentence communicates the purpose without superfluous words, making it immediately scannable.

    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?

    For a parameterless metadata tool, the description communicates the main outputs (tables, stored procedures) and implies a read-only use case. However, it does not specify edge cases or contrast with execute_query, so a small gap remains.

    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 tool has zero parameters, so the schema is trivially covered. No parameter explanation is needed, and the baseline for zero parameters is 4.

    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 uses a specific verb 'Retrieve' with a clear resource 'database schema information' and enumerates tables and stored procedures, distinguishing it from the sibling execute_query which handles data queries.

    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?

    The description implies usage for schema inspection but does not explicitly state when to prefer this over execute_query or provide exclusions. Sibling differentiation is absent, leaving the agent to infer context.

    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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