stashcat-api-mcp

create_poll

Create a poll draft, add questions and answers, then publish with custom timing and privacy options.

Instructions

Creates a new poll (initially as draft). Add questions/answers, then publish.

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": {
      "company_id": "<company_id>",
      "name": "<name>",
      "start_time": "<start_time>",
      "end_time": "<end_time>"
    },
    "name": "create_poll"
  }
}

Input Schema

Name	Required	Description
`name`	Yes	Poll name
`end_time`	Yes	End time as Unix timestamp (seconds)
`company_id`	Yes	Company ID
`start_time`	Yes	Start time as Unix timestamp (seconds)
`description`	No	Poll description
`privacy_type`	No	Privacy type (default: open)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 10, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It explicitly notes the poll is created as a draft and that questions/answers must be added later, which is important for agents to know they won't get a publishable poll immediately. It does not disclose return values or potential errors, but the core behavior is well covered.

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?

Two short sentences, front-loaded with the primary action, no filler. Every word adds value: 'draft' indicates state, and the follow-up sentence gives workflow guidance.

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 100% schema coverage and lack of output schema, the description is sufficiently complete for a create operation. It communicates the essential draft state and a high-level workflow. It could mention return value, but the schema and context make this a minor gap.

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?

Schema description coverage is 100%, so all parameters are already documented in the input schema. The description does not add additional parameter-level meaning, but this is not necessary given the high schema coverage. The workflow hint ('Add questions/answers') does not map to any specific parameter, so baseline score applies.

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?

Description clearly states 'Creates a new poll' with the specific behavior of creating it initially as a draft. This verb+resource pairing distinguishes it from sibling tools like edit_poll, publish_poll, and delete_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?

The description gives workflow context: 'Add questions/answers, then publish.' This implies that create_poll is the first step in creating a poll, and that subsequent tools (create_poll_question, create_poll_answer, publish_poll) are used after. It does not explicitly state when not to use it, but the context is clear.

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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