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mcp-twitterio

mcp-twitterio

by fuhaooo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_user_mentions

Retrieve tweets that mention a specific Twitter user, with optional time range filtering and cursor-based pagination.

Instructions

获取用户的提及

Args:
    userName: 用户名称（screen name）(必需)
    sinceTime: 在指定的unix时间戳（秒）之后
    untilTime: 在指定的unix时间戳（秒）之前
    cursor: 用于分页的游标，第一页为空字符串

Returns:
    包含用户提及的推文列表，每页返回最多20条提及

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": {
      "userName": "<userName>"
    },
    "name": "get_user_mentions"
  }
}

Input Schema

NameRequiredDescriptionDefault
cursorNo
userNameYes
sinceTimeNo
untilTimeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses key behaviors: requires a screen name, filters by unix timestamps, paginated via cursor, returns up to 20 mentions per page. It does not explicitly state read-only behavior or rate limits, but the 'get' verb and focus on retrieval imply a safe read operation. The pagination limit is important behavioral context.

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-structured with Args and Returns sections, one line per parameter, and a clear opening statement. No filler or redundant content. It is appropriately concise while conveying all necessary information.

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?

The tool has 4 parameters, no output schema, and no annotations; the description covers all parameters and the pagination/return limit. However, it does not describe the structure of the returned tweet objects (e.g., fields included), which would be helpful given the absence of an output schema. Minor gap, but overall sufficient for common use.

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?

Schema description coverage is 0%, so the description must compensate. It fully explains every parameter: userName is a screen name and required; sinceTime and untilTime are unix timestamps (seconds) bounding the range; cursor is for pagination with an empty first-page value. This adds complete semantic meaning beyond the bare schema titles.

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 '获取用户的提及' (get user mentions), which is a specific verb+resource. It distinguishes from sibling tools like get_user_last_tweets and get_tweet_replies by focusing on mentions, not the user's own tweets or replies to them.

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 implies usage context (retrieving mentions of a specific user within optional time ranges and with pagination) but does not explicitly state when to choose this tool over alternatives, nor does it name any alternative tools or exclusion criteria. It provides enough to infer use case but lacks explicit guidance.

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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