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INDIAN MEDICINES (MCP SERVER)

@nowitsidb

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その他

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nowitsidb

INDIAN MEDICINES について

A comprehensive API server for medicine information lookup, alternative suggestions, and composition analysis. This server provides multiple endpoints for searching, filtering, and analyzing medicine data with advanced features like fuzzy matching and price comparison.

#medicine#api#healthcare

INDIAN MEDICINES の設定

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": {
    "INDIAN_MEDICINE_MCP_SERVER": {
      "command": "python",
      "args": [
        "medicines_server.py"
      ]
    }
  }
}

INDIAN MEDICINES のツール

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。

概要

What is INDIAN MEDICINES (MCP SERVER)?

INDIAN MEDICINES (MCP SERVER) is a comprehensive API server for medicine information lookup, alternative suggestions, and composition analysis. It provides multiple endpoints for searching, filtering, and analyzing medicine data with features like fuzzy matching and price comparison. The server is built for healthcare professionals, patients, and researchers who need quick access to Indian medicine details, alternatives, and pricing.

How to use INDIAN MEDICINES (MCP SERVER)?

Clone the repository, install dependencies from requirements.txt, prepare a JSON data file of medicines (default path /Users/siddharthbajpai/Downloads/MCP_SERVER/medicines.json), and run python medicines_server.py. Interact with the server using any MCP client, for example via MCPClient("medicines-db") and calling endpoints like search_medicines("paracetamol") or suggest_alternatives("Dolo 650").

Key features of INDIAN MEDICINES (MCP SERVER)

  • Exact and fuzzy name matching for medicines
  • Composition/ingredient-based search and analysis
  • Multi-criteria filtering with pagination
  • Alternative medicine suggestions with price comparison
  • Prescription requirement filtering
  • Performance optimizations with multiple indexing and precomputed ingredients

Use cases of INDIAN MEDICINES (MCP SERVER)

  • Searching for a medicine by name or ingredient to get detailed info (composition, MRP, manufacturer)
  • Finding cheaper alternatives to a prescribed medicine
  • Analyzing a composition string to extract structured ingredient data
  • Filtering medicines by price range, manufacturer, or prescription requirement
  • Obtaining statistical overview and categorization of the medicines database

よくある質問

1

What does the server do?

It provides a comprehensive API for searching, filtering, and analyzing Indian medicine data, including fuzzy name matching, composition analysis, and alternative suggestions with price savings.

2

What data does the server use?

The server loads a JSON file of medicines. The default path is `/Users/siddharthbajpai/Downloads/MCP_SERVER/medicines.json`. Each medicine record includes fields like Name, Manufacturer, Composition, MRP, Prescription requirement, and processed fields like Active_Ingredients and Price_Category.

3

How many API endpoints are available?

The server exposes 15 endpoints divided into search, filter, analysis, and utility categories. Endpoints include `get_medicine_by_name`, `search_medicines`, `fuzzy_search_by_name`, `search_by_composition`, `filter_by_price_range`, `filter_by_manufacturer`, `filter_by_prescription_requirement`, `paginated_search`, `find_similar_medicines`, `analyze_composition`, `count_medicines_by_composition`, `categorize_medicines`, and `get_medicine_statistics`.

4

What are the runtime requirements?

The server runs on Python 3.8+ and uses the FastMCP framework. Dependencies include `difflib`, `re`, `collections`, `math`, `typing`, and `dataclasses`. No authentication or special transport configuration is mentioned; it communicates via the MCP protocol.

5

What are the main limitations?

The server’s performance depends on the size of the JSON dataset. Fuzzy search uses a similarity threshold (default 0.6) to control result relevance, and pagination is limited by the `page_size` parameter. The data is local and not automatically updated.

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