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barosky7834-cpu

classby-blog-mcp

by barosky7834-cpu
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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a distinct CRUD action on blog post drafts (create, update, read), with no overlapping purposes. Agents can easily distinguish them.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern: write_post_draft, update_post_draft, get_post_draft. No mixing of styles or vague verbs.

    Tool Count4/5

    Three tools is minimal but reasonable for a focused 'draft management' scope. It could be slightly thin but still fits the purpose without excess.

    Completeness3/5

    The set covers create, read, and update, but notably missing delete and list operations. Agents have no way to delete drafts or view all drafts, which may cause dead ends in workflows.

  • Average 2.8/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

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  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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

  • Behavior2/5

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

    No annotations are provided. The description only indicates a read operation (retrieve) without detailing side effects, permissions, or other behavioral traits.

    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 a single sentence, efficient but lacking detail. It is not verbose, but requires more context to be fully useful.

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

    Completeness2/5

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

    Given the simplicity of the tool (1 parameter, no output schema, no annotations), the description is still incomplete. It fails to explain parameter semantics or usage context.

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

    Parameters1/5

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

    The parameter 'id' is described only as 'by id'. With 0% schema description coverage, the description fails to add meaning such as format, constraints, or examples.

    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 clearly states the action (retrieve), resource (blog post draft), and method (by id). It is specific but does not explicitly distinguish from sibling tools like write_post_draft or update_post_draft.

    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?

    No guidance on when to use or when not. The description only implies usage when an id is available, but lacks exclusions or alternatives.

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

  • Behavior2/5

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

    No annotations exist; description only states creation but omits important behavioral traits such as whether drafts are saved, overwritten, or require authentication.

    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?

    Single sentence is concise but lacks structure; no filler but also no additional information beyond what is obvious from the name.

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

    Completeness2/5

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

    Lacks explanation of return value, side effects, or prerequisites. Given no annotations or output schema, the description is too minimal for a draft creation tool.

    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 has no descriptions for parameters (0% coverage). The description only reiterates parameter names (title/content/tags) without adding constraints, format, or usage details.

    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 Korean description clearly states 'creates a new blog post draft' with parameters title/content/tags, directly naming the action and resource. Sibling tools update_post_draft and get_post_draft further differentiate purpose.

    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?

    No guidance on when to use this tool versus updating or getting drafts. No exclusions or context provided.

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

  • Behavior2/5

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

    No annotations are provided, so the description must convey behavioral traits. It only lists updatable fields without explaining update semantics (e.g., partial vs full replacement, response behavior, or required permissions). This is insufficient for a mutation tool.

    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 sentence with no extraneous words. It efficiently conveys the core action and target fields.

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

    Completeness2/5

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

    Given the tool has 4 parameters, no output schema, and no annotations, the description is too brief. It does not explain the update effect (e.g., partial update), that the id is mandatory, or what the response contains. It is incomplete for an agent to use correctly.

    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. It names three of four parameters (title, content, tags) but not the required 'id'. No details on format, constraints, or behavior are provided. The description adds minimal value beyond the schema property names.

    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 explicitly states the verb '수정합니다' (edits) and the resource '기존 블로그 글 초안' (existing blog post draft), specifying the fields title/content/tags. This clearly distinguishes it from sibling tools write_post_draft and get_post_draft.

    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 provides no guidance on when to use this tool versus the sibling tools. It does not state that this is for updating existing drafts, while write_post_draft creates new ones and get_post_draft retrieves them. Usage context is only implicit.

    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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