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hanweg

mcp-discord

by hanweg

add_multiple_reactions

Add multiple emoji reactions to a specific message in a Discord channel by providing the channel ID, message ID, and a list of emojis.

Instructions

Add multiple reactions to a message

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channel_idYesChannel containing the message
emojisYesList of emojis to add as reactions
message_idYesMessage to react to

Implementation Reference

  • The handler logic for the 'add_multiple_reactions' tool. It fetches the channel and message using Discord client, loops through the list of emojis, adds each reaction, and returns a confirmation message.
    elif name == "add_multiple_reactions":
        channel = await discord_client.fetch_channel(int(arguments["channel_id"]))
        message = await channel.fetch_message(int(arguments["message_id"]))
        for emoji in arguments["emojis"]:
            await message.add_reaction(emoji)
        return [TextContent(
            type="text",
            text=f"Added reactions: {', '.join(arguments['emojis'])} to message"
        )]
  • Registration of the 'add_multiple_reactions' tool in the list_tools() function, including its name, description, and input schema definition.
    Tool(
        name="add_multiple_reactions",
        description="Add multiple reactions to a message",
        inputSchema={
            "type": "object",
            "properties": {
                "channel_id": {
                    "type": "string",
                    "description": "Channel containing the message"
                },
                "message_id": {
                    "type": "string",
                    "description": "Message to react to"
                },
                "emojis": {
                    "type": "array",
                    "items": {
                        "type": "string",
                        "description": "Emoji to react with (Unicode or custom emoji ID)"
                    },
                    "description": "List of emojis to add as reactions"
                }
            },
            "required": ["channel_id", "message_id", "emojis"]
        }
    ),
  • Input schema for the 'add_multiple_reactions' tool, defining parameters: channel_id, message_id, and array of emojis.
    inputSchema={
        "type": "object",
        "properties": {
            "channel_id": {
                "type": "string",
                "description": "Channel containing the message"
            },
            "message_id": {
                "type": "string",
                "description": "Message to react to"
            },
            "emojis": {
                "type": "array",
                "items": {
                    "type": "string",
                    "description": "Emoji to react with (Unicode or custom emoji ID)"
                },
                "description": "List of emojis to add as reactions"
            }
        },
        "required": ["channel_id", "message_id", "emojis"]
    }
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. While 'add' implies a mutation, it doesn't specify permissions required, rate limits, whether reactions are reversible, or what happens on failure (e.g., duplicate reactions). For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

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 a single, efficient sentence that directly states the tool's function without unnecessary words. It is appropriately sized and front-loaded, with every word contributing to understanding the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't address behavioral aspects like error handling, side effects, or response format, which are crucial for an AI agent to use the tool correctly in a Slack-like context.

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%, with clear descriptions for all three parameters (channel_id, message_id, emojis). The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints, so it meets the baseline score of 3 for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('add') and resource ('multiple reactions to a message'), making the purpose specific and understandable. However, it doesn't explicitly distinguish this tool from its sibling 'add_reaction' (which presumably adds a single reaction), leaving some ambiguity about when to choose one over the other.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'add_reaction' or other message-related tools. It lacks context about prerequisites (e.g., needing message and channel IDs) or exclusions, leaving usage decisions entirely to inference from the tool name and parameters.

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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