nc-mcp-server

create_poll

Create a poll in a Talk group conversation by providing the conversation token, question, and at least two options. The poll is announced automatically in the chat, with configurable result visibility and voting limits.

Instructions

Create a poll in a Talk conversation.

Polls can only be created in group or public conversations (not one-to-one). A chat message is automatically posted announcing the poll.

Args: token: The conversation token. Use list_conversations to find tokens. question: The poll question (max 32,000 characters). options: List of voting options (minimum 2 options required). Example: ["Yes", "No", "Maybe"] result_mode: 0 for public results (voters see results immediately after voting), 1 for hidden results (results shown only after poll is closed). Default: 0 (public). max_votes: Maximum number of options a user can vote for. 0 means unlimited (user can select all options). Default: 0.

Returns: JSON object with poll details: id, question, options, status, result_mode, max_votes.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`token`	Yes
`options`	Yes
`question`	Yes
`max_votes`	No
`result_mode`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.7.0Sep 21, 2026

TDQS

A4.8/5.0

Behavior5/5

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

The description discloses the automatic chat message, the conversation-type restriction, and result visibility behavior for result_mode. These go well beyond the minimal annotations, covering side effects and constraints.

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?

Structured with Args and Returns sections, every sentence adds essential detail. The front-loaded purpose and constraints precede parameter details, keeping it both complete and readable.

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 creation tool with 5 params, an output schema claim, and no annotation coverage, the description covers purpose, constraints, side effects, parameter semantics, and return shape. There are no obvious gaps an agent needs to call it correctly.

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?

With 0% schema description coverage, the description fully documents all five parameters: token (how to find), question (max length), options (min 2 + example), result_mode (values and meaning), max_votes (0 = unlimited). It also includes defaults.

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 opens with 'Create a poll in a Talk conversation' – a specific verb and resource. It adds constraints (group/public only) and a side effect (auto chat message), making it distinct from siblings like vote_poll/close_poll.

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?

It provides clear context: polls can only be created in group/public conversations, not one-to-one, and it points to list_conversations for finding tokens. It doesn't explicitly reference alternative creation tools, but the context is sufficient for selection.

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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