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fastmcp-me

MotherDuck DuckDB MCP Server

by fastmcp-me
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Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.8.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion or overlap. The tool's purpose is clear.

    Naming Consistency5/5

    The single tool name 'query' is a simple, imperative verb that clearly describes its action. No mixed conventions to judge.

    Tool Count3/5

    With only one tool, the server feels thin for a general-purpose database server, though it can technically execute any SQL. It falls in the borderline range for a small server.

    Completeness3/5

    The tool allows arbitrary query execution, which can cover metadata via system tables, but lacks dedicated tools for listing tables or schemas, requiring agents to know SQL to explore the database.

  • Average 3.5/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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.

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

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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.

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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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that it executes a query, but does not mention whether the query is read-only, can modify data, what permissions are needed, or how results are returned. Given the open-ended nature of SQL queries, this is a significant gap.

    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 directly states the tool's purpose without wasted words. It is concise and easy to parse.

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

    Completeness3/5

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

    Although the tool is simple and has full schema coverage, the absence of annotations and an output schema means the description should clarify expected behavior (e.g., whether it returns results or supports only SELECT queries). It does not, leaving some ambiguity. However, for a minimal query tool, it provides the core essentials.

    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 schema description covers 100% of the single parameter ('SQL query to execute that is a dialect of DuckDB SQL'), so the description adds no additional meaning. Baseline 3 is appropriate because the schema already provides sufficient semantic detail for the parameter.

    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 tool's function: 'Use this to execute a query on the MotherDuck or DuckDB database.' It specifies a verb (execute), a resource (query on a database), and distinguishes it from potential sibling tools, though none are listed.

    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 begins with 'Use this to execute a query,' giving explicit guidance on when to invoke the tool. Since there are no sibling tools or alternatives mentioned, it fully covers the main use case, though it does not discuss when not to use it or any prerequisites.

    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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