Integrations
eRegulations MCP Server
A Model Context Protocol (MCP) server implementation for accessing eRegulations API data. This server provides structured, AI-friendly access to eRegulations instances, making it easier for AI models to answer user questions about administrative procedures.
Features
- Access eRegulations data through a standardized protocol
- Query procedures, steps, requirements, and costs
- MCP prompt templates to guide LLM tool usage
- Streamlined implementation using standard I/O connections
Usage
Running with Docker (Recommended)
The recommended way to run the server is using the published Docker image from the GitHub Container Registry (GHCR). This ensures a consistent and isolated environment.
Replace https://your-eregulations-api.com
with the actual base URL of the eRegulations instance you want to connect to (e.g., https://api-tanzania.tradeportal.org
).
The server listens for MCP JSON requests on standard input and sends responses to standard output.
Example Client Configuration
Here's an example of how a client (like Claude) might be configured to use this server via Docker:
(Remember to replace the EREGULATIONS_API_URL
value in the env
section as well.)
Installation via Smithery
Alternatively, you can install and run the server using Smithery:
Visit https://smithery.ai/server/@unctad-ai/eregulations-mcp-server for the installation command.
Installation via npm Registry (Deprecated)
Running the server directly using npx
is deprecated due to potential environment inconsistencies.
~~```bash
Deprecated: Set environment variables and run with npx
export EREGULATIONS_API_URL=https://example.com/api && export NODE_ENV=production && npx -y @unctad-ai/eregulations-mcp-server@latest
You must be authenticated.
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
A Model Context Protocol server implementation that provides structured, AI-friendly access to eRegulations data, making it easier for AI models to answer user questions about administrative procedures.
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