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jonnybottles

patch-tuesday-mcp

by jonnybottles
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.9.1

  • Disambiguation5/5

    There is only one tool, so there is no risk of confusion between tools. The tool's purpose is clearly defined.

    Naming Consistency5/5

    With a single tool, naming consistency is not an issue. The name 'msrc_search' is descriptive and follows a clear convention.

    Tool Count3/5

    Having only one tool for the entire server is borderline. While the tool is comprehensive, bundling all functionality (search, CVE/KB lookup, trends) into one makes it complex and less discoverable. A small set of separate tools would be more appropriate.

    Completeness5/5

    The single tool covers all expected operations for the MSRC domain: searching, filtering, CVE/KB detail, historical trends, pagination, stats, and enrichment. There are no apparent gaps in the tool surface.

  • Average 4.8/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 54 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 failing

  • 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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  ]
}

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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

  • Behavior5/5

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

    Annotations already indicate read-only, idempotent, and open-world traits. The description adds substantial behavioral context: explains month-selection logic, cache behavior, best-effort scraping for known issues, EPSS/KEV enrichment, and error kinds. No contradictions with annotations.

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

    Conciseness3/5

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

    The description is very long, but well-structured with a summary, use-case list, parameter descriptions, and return section. While every sentence adds value, the length may hinder quick scanning for an AI agent, so conciseness is average.

    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?

    Given 40 parameters, no required params, 0% schema coverage, and a detailed output schema described, the description is exhaustive. It covers all parameter behaviors, return values, error states, edge cases (e.g., pre-release months, cache refresh), and enrichment details. No gaps.

    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?

    With 0% schema description coverage, the description fully compensates by explaining each of the 40 parameters in detail, including constraints, fast paths, default behavior, and usage notes (e.g., kb parameter supports list, month formats, product_profile expansion). This is essential for correct usage.

    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 it searches Microsoft security updates from the MSRC API, combining keyword search, CVE/KB lookup, and filtering. It explicitly distinguishes the tool's purpose with a specific verb and resource, and the lack of sibling tools does not detract from clarity.

    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?

    A bulleted list of use cases provides explicit guidance on when to use the tool, covering scenarios like getting Patch Tuesday overview, looking up CVEs, finding KB fixes, checking supersedence, known issues, and more. This is exemplary for an AI agent.

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

GitHub Badge

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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