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AIsa X / Twitter

List User Followings

get_twitter_user_followings
Read-onlyIdempotent

List the accounts a given X user follows, identified by @handle. Returns 200 entries per page with has_next_page and next_cursor. Use this to infer a user's interests, information sources, or professional network — who someone follows is usually a stronger signal of intent than who follows them. For the opposite direction use get_twitter_user_followers. To test a single specific pair without paging through thousands of records, use get_twitter_user_check_follow_relationship.

Usage-based billing; Default request estimate: $0.0036 USD (not a fixed price). Account-specific pricing and final settlement may differ.

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_twitter_user_followings"
  }
}

Input Schema

NameRequiredDescriptionDefault
cursorNoThe cursor to paginate through the results. First page is empty.
pageSizeNoThe number of followings to return per page. Provider reference (supplier schema): `{"default":200,"type":"integer"}`.
userNameYesScreen name of the user.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / required
      Previous value: -[]New value: +[
      +  "userName"
      +]
  2. Changed3 schema fields changed
    • addedInput schema / properties / cursor / description
      Added value: +"The cursor to paginate through the results. First page is empty."
    • addedInput schema / properties / pageSize
      Added value: +{
      +  "description": "The number of followings to return per page.\n\nProvider reference (supplier schema): `{\"default\":200,\"type\":\"integer\"}`.",
      +  "maximum": 200,
      +  "minimum": 20,
      +  "type": "integer"
      +}
    • addedInput schema / properties / userName / description
      Added value: +"Screen name of the user."
  3. Changed4 schema fields changed
    • removedInput schema / properties / cursor / description
      Removed value: -"The cursor to paginate through the results. First page is empty."
    • removedInput schema / properties / pageSize
      Removed value: -{
      -  "default": 200,
      -  "description": "The number of followings to return per page.",
      -  "maximum": 200,
      -  "minimum": 20,
      -  "type": "integer"
      -}
    • removedInput schema / properties / userName / description
      Removed value: -"Screen name of the user."
    • changedInput schema / required
      Previous value: -[
      -  "userName"
      -]New value: +[]
  4. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds genuine behavioral context beyond that: page size of 200 entries, the presence of has_next_page and next_cursor for paging, and that results can run to thousands of records. It stops short of stating rate limits, auth requirements, or depth limits, so it earns a 4 rather than a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core is front-loaded and efficient: what it lists, then pagination shape, then two routing alternatives. The interpretive sentence about follows being a stronger signal than followers is helpful but slightly padded, and the pricing boilerplate is not the description's own content.

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?

For a paginated read tool with a full schema, complete annotations, and an output schema that carries the return shape, the description supplies everything missing: page size, cursor semantics, direction, and the disambiguation to two sibling tools. Nothing an agent needs to call it correctly is absent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline would be 3, but the description adds the pagination contract (200 per page, has_next_page/next_cursor) that tells the agent how cursor and pageSize interact across requests. It does not add anything about the userName format beyond the handle notation, which the schema already covers.

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 states a specific verb and resource ('List the accounts a given X user follows, identified by @handle') and explicitly contrasts direction with get_twitter_user_followers. An agent can distinguish it from get_twitter_user_followers and get_twitter_user_check_follow_relationship without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It names both alternatives and the condition that selects each: the opposite direction tool for followers, and the check-relationship tool when testing a single pair instead of paging. It also gives a positive use case (inferring interests, sources, professional network), so when-to-use is fully covered.

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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