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michellepellon

mcp-server-template

by michellepellon
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    run_query executes SQL statements while describe_tables returns schema metadata. The two tools have no functional overlap and are easy to distinguish.

    Naming Consistency5/5

    Both tools follow the same lowercase verb_noun pattern: run_query and describe_tables. Naming is predictable and consistent.

    Tool Count3/5

    Two tools is on the low end of reasonable for a focused read-only database server. The set is coherent but feels thin, leaving little flexibility for more advanced exploration.

    Completeness4/5

    The pair covers the core need of read-only SQL querying with schema discovery support. Minor gaps exist, such as no ability to inspect detailed table statistics or relationships, but agents can generally accomplish their main tasks.

  • 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
    • 5 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 passing

  • 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 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 carries the burden of disclosing behavior. The verb 'List' implies a read-only, non-mutating operation, and 'queryable tables' scopes what is returned, but no details about permissions, side effects, or edge cases are provided.

    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 concise sentence with no wasted words. It is appropriately sized for a parameterless metadata-listing tool and immediately states the tool's core functionality.

    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 tool has no parameters, has an output schema, and the sibling is run_query, the description is largely adequate for selection and invocation. It could benefit from explicitly stating that it is a safe, schema-discovery step before running queries, but that omission is not critical.

    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 there is no parameter semantics to document. The description appropriately focuses on what the tool returns instead.

    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 uses a specific verb, 'List', and a clear resource, 'queryable tables and their columns'. It clearly conveys that this tool exposes metadata rather than executing queries, which distinguishes it from its sibling run_query.

    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 explicit guidance on when to use this tool versus run_query. While the name and description imply it should be used to discover available tables and columns before querying, this is left to inference.

    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?

    The description discloses the important read-only nature of the operation, which is a meaningful behavioral trait especially given there are no annotations. It also mentions bound parameters, implying parameterized execution, but it does not elaborate on query restrictions, max_rows behavior, errors, or potential overhead.

    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 with no unnecessary words. It conveys the primary action and the key constraint efficiently.

    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?

    The output schema covers return values, and the parameters are relatively simple, which reduces the burden on the description. However, the description omits guidance on alternatives and leaves max_rows semantics implicit, making it minimally complete but with clear gaps.

    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%, so the description must compensate for the lack of parameter docs. It only refers to 'bound parameters', which loosely maps to the params argument, but it does not explain the parameter placeholder format or the purpose of max_rows. The sql parameter is self-evident but not elaborated.

    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 states a specific verb ('Run') and resource ('SQL query'), and adds the key constraints 'read-only' and 'bound parameters' that distinguish it from sibling describe_tables, which is about table metadata rather than 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 the tool is for arbitrary read-only SQL execution, which gives a general sense of when to use it. However, it does not explicitly contrast with describe_tables or state when one should be preferred over the other, leaving the boundary to inference.

    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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