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ilhankilic

YaparAI MCP Server

by ilhankilic

reply_to_message

Send a reply in an existing social media conversation on Instagram, Facebook, WhatsApp, or other platforms. Provide conversation ID, account ID, and reply text.

Instructions

Reply to a social media message/DM.

Send a reply in an existing conversation on Instagram, Facebook, WhatsApp, or other connected platforms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
conversation_idYesConversation ID to reply in
account_idYesSocial account ID to reply from
messageYesReply text
org_idNoOrganization ID (uses YAPARAI_ORG_ID env var if not provided)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler function `reply_to_message` that sends a reply in an existing conversation on Instagram, Facebook, WhatsApp, or other connected platforms. It takes conversation_id, account_id, message, and optional org_id, resolves the org ID, creates a YaparAIClient, and calls client.social_reply().
    async def reply_to_message(
        conversation_id: str,
        account_id: str,
        message: str,
        org_id: str | None = None,
    ) -> dict:
        """
        Reply to a social media message/DM.
    
        Send a reply in an existing conversation on Instagram, Facebook,
        WhatsApp, or other connected platforms.
    
        Args:
            conversation_id: Conversation ID to reply in
            account_id: Social account ID to reply from
            message: Reply text
            org_id: Organization ID (uses YAPARAI_ORG_ID env var if not provided)
    
        Returns:
            Dict with reply status confirmation.
        """
        oid = resolve_org_id(org_id)
        client = YaparAIClient()
        return await client.social_reply(oid, conversation_id, account_id, {
            "message": message,
        })
  • Registration of reply_to_message as an MCP tool via `mcp.tool(reply_to_message)`.
    mcp.tool(reply_to_message)
  • Import of reply_to_message from yaparai.tools.social module into the server file where it is registered as an MCP tool.
    from yaparai.tools.social import (
        list_social_accounts,
        create_social_post,
        list_social_posts,
        get_social_quota,
        generate_caption,
        generate_hashtags,
        list_inbox,
        read_conversation,
        reply_to_message,
        ai_reply_suggestion,
    )
Behavior2/5

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

No annotations provided, so the description carries full burden. It mentions 'reply' which implies mutation, but lacks details on side effects, authentication requirements, rate limits, or whether the reply is sent immediately. The description is minimal on behavioral context.

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 sentences are front-loaded with the main action and then provide platform context. Every word earns its place; no redundancy or fluff.

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 output schema exists (not shown), the description adequately covers the tool's core purpose and parameters. It lacks usage guidelines but is otherwise complete for a simple reply tool. A higher score would require explicit guidance on alternatives.

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?

Input schema has 100% description coverage, so each parameter is already documented. The tool description adds platform context but not parameter-specific meaning beyond the schema. Baseline 3 is appropriate.

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 tool replies to social media messages/DMs in existing conversations, specifying platforms like Instagram, Facebook, WhatsApp. This is a specific verb+resource, and it distinguishes from sibling tools like create_social_post or bulk_message.

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 implies usage for replying in existing conversations, but does not explicitly state when not to use it or suggest alternatives. For instance, it doesn't mention that starting a new conversation might require a different tool, leaving some ambiguity.

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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