Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    process message special WhatsApp Business

    1

    Custom MCP bridge for structured message processing, session management, and stateful caching.

    Free

    3 usesSecurity scanned
    process message special WhatsApp Business

    process message special WhatsApp Business

    Example session with this skill installed

    Ejecuta el skill personalizado a medida con el texto 'procesar informe anual' para la sesión user_123.

    • Read your context and instructions
    • Compiled the process message special
    {
  "ok": true,
  "data": {
    "status": "processed",
    "report_id": "REP-2024-X",

    "summary": "Annual report data analyzed and stored."

      },
  "error": null
}

    Connects securely to your tools. The creator never sees your data.

    About this skill

    The problem

    LLM agents often lack a standardized way to interface with custom backend operations, leading to messy prompt loops and inconsistent data handling. Developers need a predictable bridge between natural language requests and structured execution without manual parsing overhead.

    What it does

    • Standardizes calls to the process_message MCP tool using structured JSON envelopes.
    • Manages conversational state and token isolation via session identifiers.
    • Handles errors gracefully using pre-defined codes like RATE_LIMITED and MISSING_PARAM.
    • Integrated caching mechanism to reduce redundant processing and upstream latency.
    • Ensures offline execution for operations that do not require external network access.

    Frameworks & tools

    MCP (Model Context Protocol), JSON, and local MCP server environments.

    Why this beats prompting it yourself

    Direct prompting often fails to maintain state or handle complex error codes reliably. This skill provides a schema-first approach that forces the agent to return valid JSON results while managing session memory across multiple turns, something raw prompts struggle to do consistently.

    Use cases

    • Executing custom internal business logic through a unified MCP interface.
    • Maintaining user-specific state and data cursors in long-running agent chats.
    • Building reliable wrappers for sensitive operations that require strict error reporting.
    • Reducing latency by serving repeated identical requests from the built-in cache.

    Known limitations

    Requires an active MCP server running the process_message tool. State is only maintained as long as the MCP server remains operational.

    Requires an active MCP server hosting the process_message tool. State persistence is tied to the server runtime.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Security scanned

    Checked by our 8-point scan before listing

    3 uses

    Times used to date

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    Trust & safety

    Security scan

    Security scan passed 3 months ago

    • Free with an Agensi account

    Permissions

    No special permissions declared or detected

    Listed3 months ago

    What's inside

    Frequently Asked Questions

    This skill acts as a standardized bridge between an LLM and the Model Context Protocol (MCP), ensuring that data is passed using structured JSON envelopes rather than messy, unpredictable prompts. It centralizes session management, error handling, and stateful caching for more reliable agent performance.

    This skill is compatible with any AI agent environment that supports the Model Context Protocol (MCP). It requires an active MCP server running the 'process_message' tool to execute tasks and manage conversational state successfully.

    The skill features built-in stateful caching and session identifiers that allow the agent to remember user data and conversation context across multiple turns. This prevents the agent from 'forgetting' information during long-running tasks.

    Unlike raw prompting, this skill enforces a strict schema for all inputs and outputs. It includes pre-defined error codes (like RATE_LIMITED or MISSING_PARAM), ensuring the agent handles failures gracefully instead of hallucinating a response.

    Upon purchase, you receive the production-ready MCP bridge configuration and schema definitions necessary to integrate the 'process_message' tool into your agent environment, along with documentation for setup.

    Yes, the skill includes an offline execution mode for operations that don't require external network access. This reduces latency and ensures that sensitive data processing remains local to your MCP server environment.