Patsnap Pharma Intelligence

Setup

Get your API Key at https://open.patsnap.com

Patsnap Pharma Intelligence

This skill connects your AI agent to Patsnap's Pharma Intelligence MCP server — the most comprehensive pharmaceutical data layer available for AI agents.

With this skill, your agent gains the ability to perform professional-grade pharmaceutical research: from identifying drug targets and searching clinical trials, to analyzing licensing deals and reviewing FDA labels.

Prerequisites

This skill requires the Patsnap Pharma Intelligence MCP server to be configured in your environment:

{
  "mcpServers": {
    "pharma_intelligence": {
      "url": "https://connect.patsnap.com/096456/logic-mcp?apikey=YOUR_API_KEY",
      "type": "streamableHttp"
    }
  }
}

Get your API key at open.patsnap.com.

For the full list of available tools and input parameters, refer to the official MCP server documentation: open.patsnap.com/marketplace/mcp-servers/pharma-intelligence

Instructions for AI Agents

Step 1: Always Normalize Entities First

Before executing any search or fetch operation, always call ls_ner_nor_normalize on the user's input text. This tool identifies and normalizes targets, drugs, diseases, companies, mechanisms of action (MoA), patent numbers, and clinical trial IDs — returning the internal IDs required by all downstream tools.

Do not skip this step. Raw user text must be resolved to Patsnap internal IDs for accurate retrieval.

Step 2: Choose the Right Tool for the Task

This MCP provides 28 tools organized across these domains:

• Drug & Pipeline: search and fetch drug assets, licensing and collaboration deals
• Clinical Trials: structured search, semantic search, and published results
• Scientific Literature: paper search, vector search, and translational medicine
• Patents & Regulatory: patent fetch, FDA label semantic search
• Market & Epidemiology: disease data, HEOR, financial reports, epidemiology
• Real-Time News: semantic news search and full article fetch

For the complete tool list and input parameters, see: open.patsnap.com/marketplace/mcp-servers/pharma-intelligence

Step 3: Fetch Full Records When Needed

Search tools return summary results with IDs. When the user needs complete details, always follow up with the corresponding _fetch tool (e.g., after ls_drug_search, call ls_drug_fetch with the returned IDs).

Step 4: Synthesize and Structure Your Output

After retrieving data, present findings in a structured, professional format:

• Lead with key findings: Summarize the most relevant results first
• Cite sources: Always include drug names, trial IDs, paper titles, or patent numbers
• Highlight gaps: If data is limited, acknowledge it and suggest alternative search strategies
• Use tables for comparing multiple drugs, trials, or deals side by side

Example Workflows

Biomarker Investigation

1. ls_ner_nor_normalize → extract target/disease entities
2. ls_paper_search → find supporting academic evidence
3. ls_clinical_trial_search → identify trials using this biomarker
4. ls_patent_fetch → check IP landscape

Competitive Drug Landscape

1. ls_ner_nor_normalize → normalize disease/target
2. ls_drug_search → enumerate competing assets
3. ls_drug_deal_search → identify recent BD activity
4. ls_news_vector_search → surface latest developments

Target Intelligence

1. ls_ner_nor_normalize → resolve target ID
2. ls_drug_search → find all drugs modulating this target
3. ls_paper_vector_search → retrieve mechanistic literature
4. ls_clinical_trial_search → map clinical development activity

Resources

• Patsnap Life Sciences: eureka.patsnap.com/ls-landing
• MCP Server: open.patsnap.com/marketplace/mcp-servers
• API Docs: open.patsnap.com/devportal