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Jing-yilin

TwitterAPI MCP Server

by Jing-yilin
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JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.2.0

  • Disambiguation5/5

    Every tool has a clearly distinct purpose with no ambiguity. Each targets a specific resource (tweet, user) and action (create, get, search, login), making it easy for an agent to select the correct tool. For example, get_user_by_id and get_user_by_username are clearly differentiated by the identifier type.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case throughout, such as create_tweet, get_tweet_by_id, and search_users. This predictability enhances usability and reduces confusion for agents.

    Tool Count5/5

    With 11 tools, the count is well-scoped for a Twitter API server, covering core operations like posting, retrieving, and searching tweets and users, plus authentication. Each tool earns its place without being overwhelming or insufficient.

    Completeness4/5

    The tool set provides strong coverage for read operations (get, search) and a key write action (create_tweet), with authentication handled via login_user. However, it lacks update and delete operations for tweets or user interactions (e.g., like, retweet, follow), which are common in Twitter workflows, leaving minor gaps.

  • Average 3/5 across 11 of 11 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 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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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.

GitHub Badge

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  • 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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