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EnesCinr

Twitter MCP Server

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Twitter MCP 服务器

铁匠徽章

该 MCP 服务器允许客户端与 Twitter 进行交互，从而可以发布推文和搜索 Twitter。

快速入门

  1. 创建 Twitter 开发者帐户并从Twitter 开发者门户获取 API 密钥

  2. 将此配置添加到您的 Claude Desktop 配置文件：

Windows ： %APPDATA%\Claude\claude_desktop_config.json
macOS ： ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "twitter-mcp": {
      "command": "npx",
      "args": ["-y", "@enescinar/twitter-mcp"],
      "env": {
        "API_KEY": "your_api_key_here",
        "API_SECRET_KEY": "your_api_secret_key_here",
        "ACCESS_TOKEN": "your_access_token_here",
        "ACCESS_TOKEN_SECRET": "your_access_token_secret_here"
      }
    }
  }
}

  1. 重启Claude桌面

就是这样！Claude 现在可以通过两种工具与 Twitter 进行互动：

  • post_tweet ：发布一条新推文

  • search_tweets ：搜索推文

Related MCP server: twitter-mcp

示例用法

尝试询问克劳德：

  • “你能发一条推文说‘克劳德向你问好！’吗？”

  • “你能搜索关于 Claude AI 的推文吗？”

故障排除

日志可以在以下位置找到：

  • Windows ： %APPDATA%\Claude\logs\mcp-server-twitter.log

  • macOS ： ~/Library/Logs/Claude/mcp-server-twitter.log

发展

如果您想贡献或从源代码运行：

  1. 克隆存储库：

git clone https://github.com/EnesCinr/twitter-mcp.git
cd twitter-mcp

  1. 安装依赖项：

npm install

  1. 建造：

npm run build

  1. 跑步：

npm start

运行评估

evals 包会加载一个 mcp 客户端，然后运行 index.ts 文件，因此测试之间无需重新构建。您可以通过在 npx 命令前添加前缀来加载环境变量。完整文档可在此处找到。

OPENAI_API_KEY=your-key  npx mcp-eval src/evals/evals.ts src/index.ts

执照

麻省理工学院

Available Tools

2 tools
post_tweetB

Post a new tweet to Twitter

ParametersJSON Schema
NameRequiredDescriptionDefault
textYesThe content of your tweet
reply_to_tweet_idNoOptional: ID of the tweet to reply to

TDQS

B3.3/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of disclosure. However, it only states the action without detailing any behavioral traits (e.g., authentication requirements, potential errors, rate limits, or that tweets are public). For a mutation tool, this is insufficient.

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 extremely concise at a single sentence, with no superfluous words. It directly communicates the tool's purpose without any waste.

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

Completeness3/5

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

Given the tool's simplicity (2 parameters, no output schema), the description is minimally adequate but lacks details such as expected return value (e.g., tweet ID) or any error conditions. It covers the basic action but leaves gaps for practical use.

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 input schema has 100% description coverage, so the schema already details both parameters. The description adds no extra meaning beyond what the schema provides, so it meets the baseline of 3.

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 'Post a new tweet to Twitter', which is a specific verb ('post') and resource ('tweet'). It distinguishes itself from the sibling tool 'search_tweets' by focusing on creation rather than retrieval.

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 usage guidance is provided. The description does not specify when to use this tool versus the sibling 'search_tweets', nor does it mention any prerequisites, limitations, or contextual conditions for posting a tweet.

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

search_tweetsC

Search for tweets on Twitter

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query
countYesNumber of tweets to return (10-100)

TDQS

C2.8/5.0
Behavior2/5

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

No annotations are present, so the description must bear the full burden. However, it only says 'Search for tweets' without disclosing any behavioral traits such as read-only nature, permissions required, rate limits, or result format. This is minimal disclosure.

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 concise with one sentence, but it lacks detail that could be included without significant bloat. While it is not verbose, it is also not optimally informative for its length.

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 lack of output schema and annotations, the description should compensate by explaining return values or usage constraints. It does not; it only states the basic operation, leaving many contextual gaps.

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?

Schema description coverage is 100%, so the schema already explains each parameter. The description does not add any additional meaning or context for the parameters beyond what the schema provides, so it meets the baseline.

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 tool searches for tweets on Twitter, which is a specific verb+resource. It distinguishes from the sibling tool 'post_tweet' implicitly (search vs. post), but does not explicitly state the difference.

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 is provided on when to use this tool versus the sibling 'post_tweet' or any alternatives. The description simply states what it does, without context on appropriate usage scenarios.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updatesv1.0.0
    • First observedpost_tweet
    • First observedsearch_tweets
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.2/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: one for posting tweets and one for searching tweets. There is no overlap in functionality.

Naming Consistency5/5

Both tool names follow the verb_noun pattern: 'post_tweet' and 'search_tweets'. The naming is consistent and predictable.

Tool Count3/5

With only 2 tools, the server feels thin for a Twitter API. It is borderline acceptable but lacks the breadth typically expected from a Twitter server.

Completeness2/5

The tool surface is severely incomplete. Missing fundamental operations like deleting tweets, liking, retweeting, or managing user interactions, which will cause agent failures.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Resources

Related MCP Connectors

Related MCP Servers

  • twitter-mcp

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    A
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    A lightweight Twitter MCP server for Claude Code with 17 tools for reading and writing tweets, including search, user profiles, and media uploads, using a hybrid API approach.
    19 npm
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  • twitterapi-mcp

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    A
    license
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    A Model Context Protocol (MCP) server that provides access to Twitter data through the TwitterAPI.io service, enabling Claude and other MCP clients to interact with Twitter's ecosystem without requiring Twitter developer account approval.
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  • x-mcp-server

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    A
    license
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    An MCP server that enables posting tweets, reading timelines, searching posts, and interacting with X (Twitter) API.
    15 npm
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