AverbePorto-MCP

AverbePorto‑MCP: Bridging AI Assistants and Cargo Insurance Management

The AverbePorto‑MCP server solves a common pain point for developers who need to automate interactions with the AverbePorto web platform: authenticating users, uploading XML documents, and querying ANTT protocols. Rather than building bespoke web‑scraping or REST clients for each workflow, this MCP exposes a clean, typed interface that can be consumed directly by AI assistants such as Claude or Copilot. By encapsulating the platform’s authentication flow and document handling logic behind a set of well‑defined tools, developers can focus on higher‑level business logic while the MCP handles session management, error handling, and API rate limits.

At its core, the server offers five primary tools. establishes a session and returns a token that is reused by subsequent calls, eliminating the need to embed credentials in every request. streams XML files to AverbePorto, optionally tagging them with a recipient or version identifier. lets the assistant search for protocols by key or vice‑versa, supporting JSON, XML, and CSV outputs so the downstream application can parse results in its preferred format. (incomplete in the README but implied) would fetch previously uploaded documents, while parses complex protocol identifiers into their constituent parts. These tools are deliberately minimal yet expressive, enabling AI agents to orchestrate end‑to‑end document workflows with a handful of function calls.

Developers integrate the MCP into their AI pipelines by adding a single configuration entry to the assistant’s environment. Whether using Claude Desktop, Cursor, or VS Code Copilot, the server is launched automatically and its tools become available in the assistant’s function‑calling repertoire. The integration layer also supports secure credential injection via prompts or environment variables, ensuring that API keys are never hard‑coded. This seamless startup process means a developer can prototype a new document‑processing feature in minutes, with the AI assistant handling authentication and error recovery transparently.

Real‑world scenarios that benefit from AverbePorto‑MCP include logistics firms automating cargo insurance filings, audit teams generating compliance reports, and internal tools that need to pull updated protocol data into dashboards. By exposing a consistent API surface, the MCP allows these workflows to be described declaratively in natural language prompts and executed reliably by an AI assistant. Moreover, the server’s design encourages reuse: a single instance can serve multiple projects or teams, centralizing credential management and reducing duplication of effort.

Unique advantages of AverbePorto‑MCP lie in its tight coupling to the AverbePorto platform and its focus on document‑centric operations. Unlike generic web‑scraping tools, it honors the platform’s session semantics and error codes, providing richer feedback to the AI assistant. Its modular toolset aligns with the Model Context Protocol’s expectations for stateless, repeatable calls, making it an ideal building block in complex AI workflows that span multiple external services.