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twikit-mcp

twikit-mcp

by bintangtimurlangit
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

like_tweet

Like a tweet by ID to engage with content. Enables AI assistants to favorite tweets through an authenticated Twitter/X session.

Instructions

Like (favorite) a tweet by id.

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": {
      "tweet_id": "<tweet_id>"
    },
    "name": "like_tweet"
  }
}

Input Schema

NameRequiredDescriptionDefault
tweet_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A3.5/5.0
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 behavioral disclosure. It states the action ('Like (favorite)') but does not mention authentication requirements, whether liking is reversible, how duplicate likes are handled, or what kind of response to expect. This is a clear gap for a mutating tool.

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 a single, front-loaded sentence with no filler or redundant information. Every word contributes to understanding the tool's purpose and input.

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 simplicity of the tool (one parameter, no output schema), the description is minimally viable but incomplete. It lacks usage guidelines, behavioral details, and any caveats or prerequisites. For a straightforward like action, this may suffice, but it does not fully prepare an agent for real-world invocation.

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 one required parameter tweet_id with 0% description coverage. The description's 'by id' does connect the parameter to the tweet being liked, adding some semantic value beyond the schema's bare 'Tweet Id' title. However, it does not clarify the expected format (e.g., numeric string) or any validation requirements, so it only partially compensates for the schema gap.

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 uses a specific verb 'Like (favorite)' and clearly identifies the resource ('a tweet by id'). It distinguishes itself from sibling tools like unlike_tweet, retweet, and bookmark_tweet by naming the exact action.

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 phrase 'by id' implies when to use the tool: when a tweet ID is known and the user wants to like it. However, it provides no explicit context about when not to use it or mention alternatives such as unlike_tweet for undoing the action.

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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