What makes literature search hard in the first place

Before the tool: the underlying task is harder than typing a phrase into one search box. Published research on any given mechanism or drug class is scattered across publisher platforms, indexing services, institutional repositories, and preprint servers, each with its own search syntax and coverage. Author and institution names are ambiguous — the same researcher may appear under different affiliations or name spellings across papers — so identifying who's actually leading a field takes more than a name search. And citation relationships, which tell you how influential a study is or what later work builds on it, usually aren't visible from a single database at all; they require a separate citation index. A literature search that only matches keywords also misses papers using different terminology for the same concept, which is common across sub-disciplines.

Why this needs Scientific Literature & Journals

the AI assistant or coding agent's training data stops at a fixed cutoff, and it has no native access to journal databases, institutional repositories, or live citation indices — so it can help you frame a research question but can't retrieve a current publication on its own. Scientific Literature & Journals closes that gap with a single connection that covers publication search, journal data, author affiliation, and citation lookups together, so you're not jumping between a citation index, an author directory, and a journal database separately for one research question. It runs on the same Open Platform infrastructure Patsnap has built since 2007 for connecting structured innovation data — patents, literature, and life-sciences records — to AI agents.

Prepare your query

Define the topic or mechanism you want to explore using the same terminology you'd enter into a journal database — a drug class, biological pathway, clinical indication, or combination of terms that captures the scope of your question. The server accepts a text query and an optional type filter. For this tutorial, the research question is: what published evidence exists on PD-1 inhibitor cancer immunotherapy? The input is the search text "PD-1 inhibitor cancer immunotherapy" with type set to "all" to retrieve any matching publication record.

1Connect Scientific Literature & Journals

1. Get a Patsnap Open Platform API key.
2. Copy the connection URL or JSON config from the Connect panel on the Scientific Literature & Journals page.
3. Add the server to your AI assistant or coding agent, Cursor, or another MCP-compatible client, then restart it.
4. Confirm the literature, journal, citation, and author-affiliation tools are available before you ask.

2Run the example query

Search the literature on PD-1 inhibitor cancer immunotherapy and show me the top result.

Claude invokes search_literature with the text "PD-1 inhibitor cancer immunotherapy" and type "all". The server interprets the query, ranks results by relevance, and returns publication records with metadata.

3What the result tells you

The title tells you the study design (meta-analysis) and clinical context (colorectal cancer). The DOI is a persistent identifier — click through to any standard resolver to reach the full text or citation details. The match count of 7,781 shows broad published interest in the topic and confirms this single record is the most relevant result from a large candidate pool, not an isolated hit.

4Go further with follow-up questions

From the same session, Ask your AI assistant or coding agent to:

• Find leading institutions — "Which institutions have published the most papers on PD-1 inhibitors in cancer?" surfaces potential research centers and collaboration partners.
• Identify key authors — "Who are the most cited authors in this field?" retrieves author profiles ranked by citation count.
• Narrow by indication — "Are there recent publications on PD-1 inhibitors in melanoma specifically?" refines the original search with a second clinical term.

Each follow-up builds on the evidence from the initial search, so you can move from a broad question to a prioritized reading and collaboration strategy without leaving Claude.