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mcp-twitterio

mcp-twitterio

by fuhaooo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_tweet_retweeters

Retrieve the list of users who retweeted a specific tweet. Use the tweet ID and optional cursor for pagination to get up to 100 users per page.

Instructions

获取转发推文的用户

Args:
    tweetId: 推文ID
    cursor: 用于分页的游标，第一页为空字符串

Returns:
    包含转发用户列表的字典，每页返回约100个用户

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "tweetId": "<tweetId>"
    },
    "name": "get_tweet_retweeters"
  }
}

Input Schema

NameRequiredDescriptionDefault
cursorNo
tweetIdYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full transparency burden. It discloses pagination behavior via the 'cursor' parameter, states the first page uses an empty string, and indicates the response is a dictionary with about 100 users per page. It does not mention authentication or rate limits, but for a read-only list endpoint this is reasonably transparent for an AI agent.

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 compact and well-structured: a one-line purpose followed by concise Args and Returns sections. Every sentence provides necessary information without verbosity or repetition, making it easy for an agent to parse quickly.

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?

The tool is simple with only two parameters, and the description covers the purpose, parameter semantics, return shape, pagination behavior, and page size. No output schema exists, so the description sufficiently compensates by stating the return type and content. There are no significant gaps for an agent to invoke this 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 tool description explicitly explains both parameters: 'tweetId' is the tweet ID and 'cursor' is a pagination cursor with the first page as an empty string. This adds meaning beyond the bare schema (which lacks descriptions and only lists types/defaults), fully compensating for the 0% schema description coverage.

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 the tool retrieves users who retweeted a tweet ('获取转发推文的用户'), specifying the resource (retweeters) and the verb (get). This distinguishes it from sibling tools like get_user_followers or get_tweet_replies, which target different relationships or content.

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 explains the parameters and return value, implying usage when a list of retweeters is needed for a given tweet. However, it does not explicitly contrast with alternatives or state when not to use this tool, leaving adoption to inference from the tool name and description.

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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