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volsch

md24de-mcp

by volsch
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.2.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: get_consumption_report returns full structured data, get_last_available_month returns only the month metadata, and save_pdf downloads the PDF. No overlap in functionality.

    Naming Consistency5/5

    All tool names follow a verb_noun pattern using snake_case (get_consumption_report, get_last_available_month, save_pdf). Naming is predictable and clear.

    Tool Count5/5

    With 3 tools, the server covers the essential operations for accessing consumption report data: fetching structured data, checking the latest month, and saving the PDF. The count is appropriate for the narrow domain.

    Completeness5/5

    The tool set covers all necessary operations for the server's purpose: retrieving consumption data (full report), obtaining metadata (latest month), and persisting the PDF. No obvious gaps given the domain scope.

  • Average 4.3/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 26 commits in the last 12 weeks
    • Last stable release on
    • 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": [
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  ]
}

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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

  • Behavior4/5

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

    No annotations provided, so description carries full burden. It details response structure and null handling. However, it does not explicitly state it is read-only or mention authentication or rate limits.

    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?

    Well-structured with bullet points and front-loaded purpose. Slightly verbose given an output schema exists, but every sentence adds value.

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

    Completeness5/5

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

    Complete for a no-parameter tool with output schema. Covers purpose, data source, response structure, and null handling. No missing essential information.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    No parameters exist, so schema coverage is 100%. The description adds meaning by explaining the output structure in detail, which enriches understanding beyond the empty schema.

    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 returns a heating and hot-water consumption report from messdienst24.de. It specifies the resource and distinguishes from siblings like get_last_available_month and save_pdf.

    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?

    Usage is implied but not explicit. No instructions on when to use this vs alternatives or when not to use it. Siblings suggest differentiation but are not referenced.

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

  • Behavior4/5

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

    No annotations are provided, so the description carries full burden. It discloses that the tool downloads and saves a file, with automatic filename generation. It does not cover potential side effects like overwriting existing files, required permissions, or network dependencies, but the core behavior is well described.

    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 well-structured with a summary, context, usage guidance, parameter docs, and returns. It is front-loaded with the key action. While slightly verbose with legal details, every sentence adds value.

    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's simplicity (one optional parameter, no annotations), the description covers legal context, relation to sibling tool, and alternative resource. It includes return fields. It does not mention error conditions or file overwrite behavior, but is fairly complete for its purpose.

    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 description adds minimal meaning beyond the schema for the only parameter (directory). It repeats the default and adds that the filename is set automatically, but does not elaborate on constraints like valid directories or error handling. Since schema coverage is 0%, more detail would improve usability.

    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 specific action: downloading and saving a PDF of the monthly consumption report. It identifies the document as the legally mandated UVI under German law, ties it to get_last_available_month, and distinguishes it from the resource alternative for reading/forwarding. This 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 Guidelines5/5

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

    Explicitly states when to use this tool ('when the user explicitly wants the PDF saved to a local directory') and when not to ('use the resource ... instead'). Provides a clear alternative, making it easy for the agent to decide.

    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?

    No annotations are provided, so the description carries the burden. It implies a read-only operation but does not explicitly state safety or side effect absence. For a simple query tool, this is adequate but lacks explicit behavioral disclosure.

    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 well-structured with a clear summary, context, usage guidance, and return details. It is slightly verbose with background information, but overall efficient and front-loaded.

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

    Completeness5/5

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

    Despite no parameters and no annotations, the description completely covers the tool's functionality. It explains the context, lists return fields with formats, and references sibling tools, making it self-contained for a simple query tool.

    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?

    There are no parameters, so the baseline is 4. The description adds value by detailing the return structure (year and month with formats), which goes beyond the empty schema.

    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 identifies the tool's purpose: returning the month and year of the most recently published consumption report. It uses specific verbs ('Return') and resources ('consumption report'), and distinguishes from sibling tools by noting that the full report includes this information.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

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

    The description explicitly states when to use this tool ('when you only need to know the current reporting month without fetching the full report') and mentions alternatives that contain the same information (get_consumption_report, PDF resource, save_pdf).

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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