nc-mcp-server

get_participants

Read-onlyIdempotent

Retrieve participants in a Nextcloud Talk conversation using its token. Supports pagination with limit and offset to control results.

Instructions

List participants in a Talk conversation.

Args: token: The conversation token. Use list_conversations to find tokens. limit: Maximum number of participants to return (1-200, default 50). offset: Number of participants to skip for pagination (default 0).

Returns: JSON with "data" (list of participant objects with attendee_id, actor_id, display_name, participant_type, in_call) and "pagination" (count, offset, limit, has_more).

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": {
      "token": "<token>"
    },
    "name": "get_participants"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`token`	Yes
`offset`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.7.0Sep 21, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe read operation. The description adds value by detailing the exact response shape, including the data fields (attendee_id, actor_id, display_name, participant_type, in_call) and pagination object, which goes beyond what the annotations provide.

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 well-structured with clear Args and Returns sections, and every line provides necessary information. There is no redundant prose or filler; the parameter docs and return contract are compact and immediately useful.

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 simple read-only paginated list endpoint with one required parameter, the description is complete. It covers token acquisition, pagination parameters, and the response structure, so the agent can invoke the tool correctly without additional inference. The annotations cover the safety profile, and the output schema covers detailed return validation.

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 carries full responsibility for parameter meaning. It explains token (with guidance to use list_conversations), limit (with range 1-200 and default 50), and offset (with pagination purpose and default 0). This fully compensates for the bare input schema and gives an agent everything needed to use parameters correctly.

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 a specific verb and resource: 'List participants in a Talk conversation.' This clearly distinguishes it from sibling tools like get_conversation, get_messages, and list_conversations, which operate on different resources or actions. An agent can confidently select this tool for retrieving participant 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 gives useful context by telling the agent to use list_conversations to find tokens, which addresses a common prerequisite. However, it does not explicitly state when to prefer this tool over alternatives or when not to use it. Usage is implied by the purpose rather than explicitly contrasted with sibling tools.

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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