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Pharma Intelligence

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30 toolsv1.0.0
Overview

About

Pharma intelligence endpoint offering structured search, semantic vector search, and entity retrieval across clinical trials, drugs, targets, diseases, patents, papers, deals, FDA labels, epidemiology, HEOR, financial reports, news, and translational medicine.

Capabilities

1 / 3

ls_web_search

Search the web for current information. Use this tool when the user wants to search the web for current information on a specific topic. It returns a list of relevant web results.

Arguments
query* Requiredstring

str. Search query string.

ls_translational_medicine_search

Search translational medicine information. Use this tool when the user wants to find translational medicine records by drug, target, disease, sponsor organization, or publication window. It works well for structured follow-up queries after entity extraction, such as narrowing results to a specific disease area, sponsor, or publication window.

/api/ls/translational_medicine/search

ls_translational_medicine_fetch

Batch fetch full detail records by translational medicine IDs.

/api/ls/translational_medicine/fetch

ls_target_fetch

Batch fetch full detail records by target IDs or target names.

/api/ls/target/fetch

ls_patent_vector_search

Search patents with vector similarity. Use semantic similarity to search patents. This is useful for complex natural-language queries. It calls the unified vector search interface and returns relevant text chunks.

/api/ls/patent/vector_search

ls_patent_search

Search patent information. Use this tool when the user wants to retrieve patent records by drug, target, disease, applicant organization, patent category, or publication time. It is useful for structured patent landscaping queries such as "ADC patents in CN after 2020" or "PD-1 related patents from Merck".

/api/ls/patent/search

ls_patent_fetch

Batch fetch full detail records by patent IDs or patent numbers.

/api/ls/patent/fetch

ls_paper_vector_search

Search academic papers with vector similarity. Use semantic similarity to search academic papers. This is useful for complex natural-language queries. It calls the unified vector search interface and returns relevant text chunks.

/api/ls/paper/vector_search

ls_paper_search

Search academic papers. Use this tool when the user wants to find papers by drug, target, disease, organization, journal, author, or publication year. It is effective for evidence-finding queries such as "recent Nature Medicine papers about KRAS inhibitors" or "papers from Dana-Farber on PD-1".

/api/ls/paper/search

ls_paper_fetch

Batch fetch full detail records by paper IDs.

/api/ls/paper/fetch

What is Pharma Intelligence MCP Server?

Pharma intelligence endpoint offering structured search, semantic vector search, and entity retrieval across clinical trials, drugs, targets, diseases, patents, papers, deals, FDA labels, epidemiology, HEOR, financial reports, news, and translational medicine.

Capabilities

  • Search the web for current information. Use this tool when the user wants to search the web for current information on a specific topic. It returns a list of relevant web results.
  • Search translational medicine information. Use this tool when the user wants to find translational medicine records by drug, target, disease, sponsor organization, or publication window. It works well for structured follow-up queries after entity extraction, such as narrowing results to a specific disease area, sponsor, or publication window.
  • Batch fetch full detail records by translational medicine IDs.
  • Batch fetch full detail records by target IDs or target names.
  • Search patents with vector similarity. Use semantic similarity to search patents. This is useful for complex natural-language queries. It calls the unified vector search interface and returns relevant text chunks.
  • Search patent information. Use this tool when the user wants to retrieve patent records by drug, target, disease, applicant organization, patent category, or publication time. It is useful for structured patent landscaping queries such as "ADC patents in CN after 2020" or "PD-1 related patents from Merck".
  • Batch fetch full detail records by patent IDs or patent numbers.
  • Search academic papers with vector similarity. Use semantic similarity to search academic papers. This is useful for complex natural-language queries. It calls the unified vector search interface and returns relevant text chunks.
  • Search academic papers. Use this tool when the user wants to find papers by drug, target, disease, organization, journal, author, or publication year. It is effective for evidence-finding queries such as "recent Nature Medicine papers about KRAS inhibitors" or "papers from Dana-Farber on PD-1".
  • Batch fetch full detail records by paper IDs.
  • Fetch drug pipeline records for an organization. Use either organization_id or organization name. Must provide exactly one of them.
  • Batch fetch full detail records by organization IDs or organization names.
  • Search news with vector similarity. Use semantic similarity to search news content and return the most relevant text chunks. This is suitable for natural-language queries and can match semantic intent rather than only keywords.
  • Batch fetch full detail records by news IDs.
  • Normalize entities from user query text. Identify and normalize entities such as targets, drugs, diseases, companies, drug types, mechanisms of action, action types, patent numbers, and clinical trial numbers from the input text. Runs NER-NOR API and LLM keyword extraction in parallel, then merges and deduplicates the results via autocomplete.
  • Search financial reports with vector similarity. Use semantic similarity to search financial reports and prospectuses. This is useful for complex natural-language queries. It calls the unified vector search interface and returns relevant text chunks.
  • Search FDA labels with vector similarity. Use semantic similarity to search FDA drug labels. This is useful for complex natural-language queries. It calls the unified vector search interface and returns relevant text chunks.
  • Search epidemiology data with vector similarity. Use semantic similarity to search epidemiology content. This is useful for complex natural-language queries. It calls the unified vector search interface and returns relevant text chunks.
  • Search drug information. Use this tool when the user wants to find drug assets by drug name, target, disease, organization, development phase, or milestone date. It is well suited for structured pipeline filtering, such as "PD-1 drugs in Phase 3 in China" or "approved drugs from a specific company".
  • Fetch drug R&D milestone history records (timeline) for a specific drug. Use either `drug_id` (UUID) or `drug` (drug name). Either drug_id or drug must be provided.
  • Batch fetch full detail records by drug IDs or drug names.
  • Search drug deal information. Use this tool when the user wants to find licensing, collaboration, acquisition, or option deals related to a drug, target, disease, or company. It is useful for structured business development queries such as "ADC licensing deals in US after 2021" or "Phase 2 KRAS deals involving Novartis".
  • Batch fetch full detail records by drug deal IDs.
  • Batch fetch full detail records by disease IDs or disease names.
  • Search clinical trials with vector similarity. Use semantic similarity to search clinical trials. This is useful for complex natural-language queries. It calls the unified vector search interface and returns relevant text chunks.
  • Search clinical trial information. Use this tool when the user wants to retrieve registered clinical trials by study drug, control drug, target, disease, sponsor organization, phase, or geography. It is suitable for structured trial filtering such as "Phase 2 EGFR trials in US" or "PD-1 studies sponsored by Merck".
  • Search clinical trial result information. Use this tool when the user wants to retrieve published or indexed clinical trial results by drug, target, disease, sponsor, phase, or evaluation outcome. It is useful for result-centric questions such as "Phase 3 PD-1 trial results in melanoma" or "positive EGFR trial readouts after 2022".
  • Batch fetch full detail records by clinical trial result IDs.
  • Batch fetch full detail records by clinical trial IDs or registration numbers.
  • Search clinical guidelines with vector similarity. Use semantic similarity to search clinical guidelines. This is useful for complex natural-language queries. It calls the unified vector search interface and returns relevant text chunks.

How to connect

Get a Patsnap Open Platform API key, copy the MCP connection URL from the Connect panel on this page, and add the server to Cursor, Claude Desktop, or another Model Context Protocol-compatible AI agent client.

Example MCP configuration

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

Use cases

  • Use Pharma Intelligence MCP Server to let AI agents access Patsnap innovation intelligence inside agent conversations.

FAQ

What is Pharma Intelligence MCP Server?
Pharma Intelligence MCP Server is a Patsnap Open Platform Model Context Protocol server that exposes innovation intelligence tools for AI agents and LLM clients.
How does Pharma Intelligence MCP Server work with AI agents?
An MCP-compatible client connects to the server URL with your Patsnap Open Platform API key, discovers available tools, and lets the model call Patsnap data and capabilities during conversations.
What tasks can Pharma Intelligence MCP Server perform?
Pharma Intelligence MCP Server exposes Patsnap tools through the Model Context Protocol so AI agents can retrieve patents, papers, and innovation intelligence during multi-step reasoning.
How do I connect this MCP server to an agent?
Get a Patsnap Open Platform API key, copy the MCP connection URL or JSON config from this page, and add it to your MCP-compatible client following the MCP Quick Start guide.