Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Alumnx MCP Server what are the symptoms of powdery mildew on grapes?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server for agricultural intelligence, providing tools for pest/disease lookup, government scheme discovery, and SME knowledge retrieval via semantic search.
Pests & Diseases — RAG-powered lookup for crop pest and disease information
Government Schemes — RAG-powered search for agriculture-related government schemes by type and state
SME Divesh — Semantic search over a Pinecone knowledge base (SME-Divesh namespace)
MCP + REST — Dual interface: MCP protocol (via FastMCP) and plain REST (/callTool)
Related MCP server: Government Scheme MCP Server
Python 3.9+
Dependencies (install via pip install -r requirements.txt):
fastmcp
pinecone
sentence-transformers
python-dotenv
uvicorn
httpx
fastapiCreate a .env file in the project root with the following variables:
Variable | Required | Description |
| ✅ | Base URL of the Pests & Diseases RAG service |
| ✅ | Base URL of the Government Schemes RAG service |
| ✅ | API key for Pinecone |
| ✅ | Name of the Pinecone index to query |
| ❌ | HTTP timeout in seconds for RAG calls (default: |
Example .env:
PESTS_DISEASES_RAG_URL= ___URL__
GOVT_SCHEMES_RAG_URL= __URL__
PINECONE_API_KEY=your-pinecone-api-key
PINECONE_INDEX=your-index-name
RAG_TIMEOUT=30python alumnx_mcp_server.pyThe server starts on port 9000 by default.
Endpoint | Description |
| MCP protocol endpoint (FastMCP) |
| REST tool call endpoint |
| Lists all available tools |
| Health check |
pests_and_diseasesQuery the RAG system for information about pests and diseases affecting crops.
Parameter | Type | Required | Default | Description |
| string | ✅ | — | Name of the pest or disease |
| string | ❌ |
| Crop affected by the pest or disease |
Example response:
{
"status": "success",
"information": "...",
"sources": ["..."]
}govt_schemesQuery the RAG system for government schemes related to agriculture.
Parameter | Type | Required | Default | Description |
| string | ✅ | — | Type or topic of the scheme |
| string | ❌ |
| State for which to retrieve schemes |
Example response:
{
"status": "success",
"information": "...",
"sources": ["..."]
}sme_diveshSemantic search over the SME-Divesh Pinecone namespace using all-MiniLM-L6-v2 embeddings.
Parameter | Type | Required | Default | Description |
| string | ✅ | — | The search query |
| integer | ❌ |
| Number of top results to return |
Example response:
{
"status": "success",
"query": "crop irrigation techniques",
"results": [
{
"score": 0.91,
"text": "...",
"source": "...",
"chunk_index": 2
}
]
}All tools are also callable via the /callTool POST endpoint:
curl -X POST http://localhost:9000/callTool \
-H "Content-Type: application/json" \
-d '{
"name": "pests_and_diseases",
"arguments": {
"pest_name": "aphids",
"crop": "wheat"
}
}'┌─────────────────────────────────────────┐
│ Alumnx MCP Server │
│ │
│ FastAPI App │
│ ├── /mcp ← FastMCP (MCP) │
│ ├── /callTool ← REST interface │
│ ├── /list-tools ← Tool discovery │
│ └── /health ← Health check │
│ │
│ Tools │
│ ├── pests_and_diseases → RAG HTTP call │
│ ├── govt_schemes → RAG HTTP call │
│ └── sme_divesh → Pinecone │
└─────────────────────────────────────────┘This server cannot be deployed
Knowledge graph ingestion, entity search, ontology analysis, and CoSync scoring.
Semantic patent search & analysis: find, compare, and analyze patents by meaning.
Semantic search for people, projects and AI agents by task, skills and collaboration needs.
Curated knowledge API for AI agents - skill packs, semantic search, validated patterns.
