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andrewwgordon

mcp-chinookdb-server

by andrewwgordon
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so no ambiguity whatsoever.

    Naming Consistency5/5

    Single tool uses clear snake_case verb_noun pattern, perfectly consistent.

    Tool Count3/5

    One tool is on the low end; while it covers the core querying need, a few additional tools for schema discovery would improve utility.

    Completeness3/5

    Provides the essential SELECT query capability but lacks metadata tools (e.g., list tables, describe columns), which agents may need to form correct queries.

  • Average 3.7/5 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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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

  • Behavior3/5

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

    With no annotations, the description must disclose behavioral traits. It states the tool is read-only, only accepts SELECT queries, and returns a formatted string or error. This covers safety and output format but lacks details on authentication, rate limits, or error handling specifics. The information is adequate but not exhaustive.

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

    Conciseness4/5

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

    The description is three sentences: purpose/constraint, re-emphasis of constraint, and args/returns. It is brief and front-loads the key information. Minor redundancy ('read-only SQL query' and 'Only SELECT statements allowed') could be merged, but overall it is efficient.

    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 tool (one parameter, no output schema), the description covers the essential: what it does, the constraint (SELECT only), and return format. It does not mention the database is a sample or any connection details, but for the tool's complexity, it is sufficiently complete.

    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 has 0% description coverage, so the description must compensate. The description explains the 'sql_query' parameter as 'The SQL SELECT query to execute,' which clarifies it must be a SELECT. However, it does not specify formatting, escaping, or length constraints. This adds some meaning but not comprehensive detail.

    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 read-only SQL SELECT queries against the Chinook database. It specifies the verb (executes), resource (Chinook database), and action type (SELECT only), which is specific and unambiguous.

    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 states that only SELECT statements are allowed, which implies read-only usage. However, it does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention when not to use it. Since no sibling tools exist, the lack of comparison is less critical, but the guideline is still minimal.

    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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