What environment variable is required?
`EREGULATIONS_API_URL` is required and must point to the base URL of the target eRegulations API (e.g., `https://api-tanzania.tradeportal.org`).
@MCP-Mirror
基本情報
Mirror of
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"unctad-ai_eregulations-mcp-server": {
"command": "docker",
"args": [
"pull",
"ghcr.io/unctad-ai/eregulations-mcp-server:latest"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
eRegulations MCP Server is a Model Context Protocol (MCP) server that provides structured, AI‑friendly access to eRegulations API data, enabling AI models to answer user questions about administrative procedures.
Run via Docker (recommended): set the environment variable EREGULATIONS_API_URL to your eRegulations API base URL, then run the published Docker image from GHCR with docker run -i --rm -e EREGULATIONS_API_URL ghcr.io/unctad-ai/eregulations-mcp-server. The server listens for MCP JSON requests on stdin and sends responses to stdout. Installation via Smithery is also available; running directly with npx is deprecated.
listProcedures, getProcedureDetails, getProcedureStep, searchProcedures.`EREGULATIONS_API_URL` is required and must point to the base URL of the target eRegulations API (e.g., `https://api-tanzania.tradeportal.org`).
The recommended method is via Docker: set the environment variable and run `docker run -i --rm -e EREGULATIONS_API_URL ghcr.io/unctad-ai/eregulations-mcp-server:latest`. A client configuration example for using Docker is provided in the README.
The server exposes four tools: `listProcedures` (list all procedures), `getProcedureDetails` (get details by `procedureId`), `getProcedureStep` (get a step by `procedureId` and `stepId`), and `searchProcedures` (search by keyword; note: this searches related objectives and may include results beyond direct procedure names).
Running via `npx` is deprecated due to potential environment inconsistencies. The recommended approach is Docker (or installation via Smithery).
The server provides MCP prompt templates that guide LLMs in using the available tools correctly. Clients that support the prompt templates capability automatically receive them to improve API interaction.
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