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Anuragck

Dev Blog MCP Server

by Anuragck
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or misselection. The tool's purpose is clear from its name and description.

    Naming Consistency4/5

    The single tool name 'publish_blog_to_devto' is descriptive and follows a verb-object pattern, though it's somewhat verbose. Since there is only one tool, consistency is trivially maintained.

    Tool Count1/5

    A single tool for a blog publishing server is extremely thin. Typical blog workflows require listing, editing, and deleting posts, so one tool is insufficient for the apparent scope.

    Completeness1/5

    The tool only supports publishing, with no ability to retrieve, update, or delete existing posts. This leaves significant gaps in the content lifecycle and would force agents to use external APIs.

  • Average 4.1/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits 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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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, so the description carries the full burden of disclosing behavior. It explains that the tool publishes or saves a draft and returns a success/failure message with the article URL. However, it does not mention prerequisites like authentication, the public/irreversible nature of publishing, or potential side effects (e.g., modifying existing posts). This is adequate but not exhaustive.

    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 well-organized with a one-line summary, a bullet-like Args list, and a Returns section. Each line provides essential information without fluff or redundancy. It is front-loaded with the core purpose, making it easy for an agent to quickly grasp the tool's function and parameters.

    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 complexity (7 parameters, no annotations), the description covers all parameters and the return value adequately. It does not mention prerequisites like API authentication or rate limits, but these may be implicit for a dev.to integration. The presence of an output schema and the Returns section reduce the need for additional return-value explanation. Overall, it is sufficiently complete for an agent to use the tool correctly.

    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?

    The input schema has no parameter descriptions (0% coverage), so the description's Args section is the sole source of parameter meaning. It explains every parameter clearly, including the distinction for 'published' (True for immediate, False for draft), examples for 'tags', and the purpose of 'canonical_url' (cross-posting). This exceeds the schema's minimal type/title information.

    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 begins with a clear, action-oriented statement: 'Publishes a blog post to dev.to.' This specifies the exact verb, resource, and target platform, leaving no ambiguity about the tool's function. Since there are no sibling tools, there is no differentiation needed, but the purpose is fully clear.

    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 does not explicitly state when to use this tool or provide alternatives, but the tool name and opening sentence make its usage obvious. It lacks guidance on edge cases (e.g., when to use draft vs. publish) beyond parameter semantics, so usage context is only implied rather than explicitly directed.

    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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