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tangivis

twikit-mcp

by tangivis

vote

Cast a vote on an X poll by supplying the poll's card URI, name, tweet ID, and the choice you want to select.

Instructions

Vote on an X poll.

X polls have 2-4 choices and a duration in minutes. The card_uri and card_name come from the tweet's poll card metadata (obtainable via get_tweet).

Args: selected_choice: The label of the choice to vote for (must be non-empty). card_uri: The poll card URI (from the tweet's poll card metadata). tweet_id: The ID of the tweet containing the poll. card_name: The name of the poll card (from the tweet's poll card metadata).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
card_uriYes
tweet_idYes
card_nameYes
selected_choiceYes

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 of behavioral disclosure. It only says 'Vote on an X poll' without disclosing side effects, reversibility, rate limits, or the possibility of failure (e.g., poll closed, already voted). The metadata source information is helpful but does not address the behavioral implications of casting a vote.

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 starts with a clear one-line purpose and then provides necessary context and a structured argument list. It is efficient, with no redundant fluff. The repetition between the prose and the arg list is minor and serves to clarify each parameter, so it remains appropriately concise.

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?

Given that there are no annotations and the output schema is not shown, the description covers all parameters and adds useful domain context (poll structure, metadata source). It could mention potential failure conditions or the action's irreversibility, but it is otherwise complete enough for a relatively simple voting action.

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 for the lack of parameter details. It fully compensates by explaining each of the four parameters: selected_choice is the label and must be non-empty, card_uri and card_name come from poll card metadata, and tweet_id identifies the tweet containing the poll. This adds critical meaning beyond the bare schema.

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 tool's action with 'Vote on an X poll,' a specific verb and resource. It distinguishes itself from siblings like create_poll by focusing solely on voting on an existing poll. The additional context about poll structure (2-4 choices, duration) reinforces the purpose.

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

Usage Guidelines4/5

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

The description provides clear context by stating that card_uri and card_name come from the tweet's poll card metadata and are obtainable via get_tweet, implying the need to fetch the tweet first. It does not explicitly mention alternatives or exclusion scenarios, but the guidance on prerequisites is clear and actionable.

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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