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twikit-mcp

twikit-mcp

by tangivis
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_list_tweets

Fetch tweets from a specified Twitter list, with pagination support using a cursor to retrieve additional batches.

Instructions

Get tweets from a Twitter List (paginated).

Args: list_id: The list ID. count: Number of tweets to fetch (default 20, max 100). cursor: Pagination cursor from a previous response's next_cursor.

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": {
      "list_id": "<list_id>"
    },
    "name": "get_list_tweets"
  }
}

Input Schema

NameRequiredDescriptionDefault
countNo
cursorNo
list_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.1.39
    • addedInput schema / properties / list_id / anyOf
      Added value: +[
+  {
+    "type": "integer"
+  },
+  {
+    "type": "string"
+  }
+]
    • removedInput schema / properties / list_id / type
      Removed value: -"string"
  2. First observedv0.1.33

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of disclosing behavior. It does mention pagination via cursor, adding useful behavioral context, but it does not explicitly state side-effect safety, authentication requirements, or rate limits. For a read-only getter, this is acceptable but not comprehensive.

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, front-loading the main purpose in a single sentence followed by a clear Args list. Every line adds value with no redundant filler.

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 is simple and the description covers all parameters and the key pagination behavior. The presence of an output schema means return values need not be described. However, it lacks explicit guidance on when to use this instead of other tweet-fetching tools, so it is not fully complete.

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 schema has 0% description coverage, so the description provides all parameter meaning. It explains list_id as the list ID, count with default (20) and max (100), and cursor as a pagination token from a previous response's `next_cursor`. This goes well beyond the bare schema definitions.

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 verb 'Get' and the resource 'tweets from a Twitter List', which precisely defines the tool's purpose. This distinguishes it from sibling tools like get_list or get_list_members, which operate on list metadata or members, not tweets.

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 through its straightforward verb-noun structure, but it does not explicitly provide alternative tools or when-not-to-use scenarios. Sibling tools like get_timeline or get_user_tweets are not mentioned, leaving the agent to infer when list tweets are preferable.

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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