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olivier-motium

x-ai-mcp

x_followers

Retrieve follower lists for X (Twitter) accounts to analyze social connections and manage relationships. Specify a username and count to view followers.

Instructions

List followers for yourself or another user.

Args:
    username: Username or user ID (default: your account)
    count: Number of followers (1-1000, default 50)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
usernameNo
countNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states it's a list operation but doesn't disclose behavioral traits such as rate limits, authentication needs, pagination, or what the output looks like. This is inadequate for a tool with potential API constraints.

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 description is front-loaded with the core purpose, followed by parameter details in a structured format. It's efficient with two sentences and a bullet-like list, though the parameter explanations could be integrated more smoothly.

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 no annotations, 0% schema coverage, but an output schema exists, the description is partially complete. It covers the purpose and parameters but lacks behavioral context and doesn't leverage the output schema to explain return values, leaving gaps for agent usage.

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 description coverage is 0%, but the description compensates by explaining both parameters: 'username' (with default behavior) and 'count' (with range and default). It adds meaningful context beyond the bare schema, though it could detail format constraints for 'username'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('List') and resource ('followers'), specifying it can be for yourself or another user. However, it doesn't explicitly differentiate from sibling tools like 'x_following' or 'x_user_info', which might provide overlapping user-related data.

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 by mentioning 'yourself or another user,' but provides no explicit guidance on when to use this tool versus alternatives like 'x_user_info' or 'x_following.' It lacks context on prerequisites or exclusions.

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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