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JacobStephens2

Magisterium MCP Server

Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Only one tool exists, so there is no potential for confusion between tools. The tool's purpose is clearly defined and distinct.

    Naming Consistency5/5

    The tool name follows a clear verb_noun structure with 'magisterium_query' indicating the domain and action. With a single tool, there is no inconsistency to worry about.

    Tool Count4/5

    The server has a single tool, which is slightly under the typical range but appropriate for its narrow scope of querying the Magisterium API. One tool feels justified given the focused purpose.

    Completeness4/5

    The tool covers the core need of querying for authoritative Church teaching responses with citations. Additional tools could explore related resources, but the current surface is functional for its intended use.

  • Average 4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 2 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.

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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?

    No annotations are provided, leaving the description to carry the transparency burden. It adds some behavioral context by indicating the responses are 'authoritative' and include 'citations', but it does not disclose potential side effects, permissions, rate limits, or return format. For a simple query tool, this is adequate but not rich.

    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, well-structured sentence. It is front-loaded with the main action and provides the key benefit ('with citations') without extraneous detail. Every word earns its place.

    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 lack of an output schema and sibling tools, the description provides sufficient context to understand the tool's purpose and likely return value (cited teaching responses). It is complete for a simple query tool, though it could optionally mention the response format or limitations.

    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 description coverage is 100%, and all parameters already have descriptive text. The tool description itself adds no additional parameter semantics beyond restating that it sends a query. A baseline score of 3 is appropriate since the schema does the heavy lifting.

    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's verb ('Send a query'), resource ('Magisterium API'), and expected outcome ('authoritative Catholic Church teaching responses with citations'). It is specific and leaves no ambiguity about what the 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 Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    While no explicit alternatives or exclusions are given, the description clearly implies when to use this tool: whenever the user needs authoritative Catholic Church teachings. The context is sufficiently clear for an agent to determine appropriate usage, though it lacks explicit 'when not to use' guidance.

    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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