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Sealjay

mcp-whatsapp

send_poll_vote

Destructive

Vote on WhatsApp polls by selecting options to participate in group decisions or change your previous vote.

Instructions

Cast (or re-cast) a vote on a previously-seen poll. Each call replaces the caller's prior vote on that poll; options must match the poll's option names exactly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chat_jidYesWhatsApp JID: individual as `<digits>@s.whatsapp.net` or bare phone digits, group as `<digits>-<timestamp>@g.us`
optionsYesoption names to pick (1–32); must match the poll exactly
poll_message_idYesID of the poll message to vote on
Behavior4/5

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

The description adds valuable behavioral context beyond annotations: it explains that votes are replaceable ('Each call replaces the caller's prior vote'), specifies exact matching requirements ('options must match the poll's option names exactly'), and implies mutation (consistent with destructiveHint=true). While annotations cover safety traits, the description enhances understanding of voting mechanics without contradiction.

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 extremely concise (two sentences) and front-loaded with the core purpose. Every sentence earns its place: the first defines the action and resource, the second clarifies behavioral constraints. There is zero wasted text or redundancy.

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 the tool's moderate complexity (mutation with three parameters), rich annotations (covering safety and idempotency), and no output schema, the description is nearly complete. It explains the voting behavior and constraints well, but doesn't detail error cases (e.g., invalid poll ID) or response format, leaving minor gaps for a destructive operation.

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?

With 100% schema description coverage, the input schema already fully documents all three parameters (chat_jid, options, poll_message_id). The description adds marginal value by reinforcing the exact matching requirement for 'options' and implying the poll must be 'previously-seen', but doesn't provide additional syntax or format details beyond the schema. This meets the baseline for high schema coverage.

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 specific action ('Cast (or re-cast) a vote') on a specific resource ('previously-seen poll'), distinguishing it from sibling tools like 'send_poll' (which creates polls) and 'get_poll_results' (which retrieves results). The verb 'cast/re-cast' precisely defines the operation.

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 about when to use this tool ('on a previously-seen poll') and mentions the replacement behavior ('Each call replaces the caller's prior vote'), but it doesn't explicitly state when NOT to use it or name alternatives like 'get_poll_results' for viewing results. The guidance is contextual but lacks explicit 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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