one_health_analysis: Скилл для комплексного анализа генома

--- name: one_health_analysis description: "One Health Pathogen Analysis - One Health analysis: pathogen genome, cross-species gene comparison, antimicrobial drugs, and environmental context. Use this skill for one health tasks involving get genome dataset report by taxon get homology symbol get mechanism of action by drug name tavily search get taxonomy. Combines 5 tools from 4 SCP server(s)." --- # One Health Pathogen Analysis **Discipline**: One Health | **Tools Used**: 5 | **Servers**: 4 ## Description One Health analysis: pathogen genome, cross-species gene comparison, antimicrobial drugs, and environmental context. ## Tools Used - **`get_genome_dataset_report_by_taxon`** from `ncbi-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI` - **`get_homology_symbol`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` - **`get_mechanism_of_action_by_drug_name`** from `fda-drug-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug` - **`tavily_search`** from `search-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search` - **`get_taxonomy`** from `ncbi-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI` ## Workflow 1. Get pathogen genome data 2. Compare virulence genes across species 3. Get antimicrobial mechanism 4. Search environmental context 5. Get taxonomy classification ## Test Case ### Input ```json { "taxon": "Salmonella", "gene": "invA", "drug": "ciprofloxacin" } ``` ### Expected Steps 1. Get pathogen genome data 2. Compare virulence genes across species 3. Get antimicrobial mechanism 4. Search environmental context 5. Get taxonomy classification ## Usage Example > **Note:** Replace `sk-b04409a1-b32b-4511-9aeb-22980abdc05c` with your own SCP Hub API Key. You can obtain one from the [SCP Platform](https://scphub.intern-ai.org.cn). ```python import asyncio import json from contextlib import AsyncExitStack from mcp import ClientSession from mcp.client.streamable_http import streamablehttp_client from mcp.client.sse import sse_client SERVERS = { "ncbi-server": "https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", "ensembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", "fda-drug-server": "https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", "search-server": "https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search" } async def connect(url, stack): transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "sk-b04409a1-b32b-4511-9aeb-22980abdc05c"}) read, write, _ = await stack.enter_async_context(transport) ctx = ClientSession(read, write) session = await stack.enter_async_context(ctx) await session.initialize() return session def parse(result): try: if hasattr(result, 'content') and result.content: c = result.content[0] if hasattr(c, 'text'): try: return json.loads(c.text) except: return c.text return str(result) except: return str(result) async def main(): async with AsyncExitStack() as stack: # Connect to required servers sessions = {} sessions["ncbi-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", stack) sessions["ensembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", stack) sessions["fda-drug-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", stack) sessions["search-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", stack) # Execute workflow steps # Step 1: Get pathogen genome data result_1 = await sessions["ncbi-server"].call_tool("get_genome_dataset_report_by_taxon", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Compare virulence genes across species result_2 = await sessions["ensembl-server"].call_tool("get_homology_symbol", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Get antimicrobial mechanism result_3 = await sessions["fda-drug-server"].call_tool("get_mechanism_of_action_by_drug_name", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Search environmental context result_4 = await sessions["search-server"].call_tool("tavily_search", arguments={}) data_4 = parse(result_4) print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}") # Step 5: Get taxonomy classification result_5 = await sessions["ncbi-server"].call_tool("get_taxonomy", arguments={}) data_5 = parse(result_5) print(f"Step 5 result: {json.dumps(data_5, indent=2, ensure_ascii=False)[:500]}") # Cleanup print("Workflow complete!") if __name__ == "__main__": asyncio.run(main()) ```