Patsnap Patent & Literature Search
The Patsnap Patent & Literature Search server gives AI agents read-only access to Patsnap's databases of 200M+ patents and 216M+ scientific papers, enabling comprehensive R&D research.
Search (
patsnap_search): Search patents or scientific literature using natural language/semantic queries, BM25 keywords, and structured filters (assignee, inventor, IPC, legal status, jurisdiction, date range, citation count). Supports pagination and sorting. Combine separate patent and paper searches for dual-corpus analysis.Fetch (
patsnap_fetch): Retrieve full records in Markdown using result URLs or publication numbers. Access bibliographic data, claims, descriptions, drawings (signed URLs), legal status, patent family, and citations. Supports gzip compression and up to 100 records per call.
Enables searching and retrieving scientific preprints from arXiv via Patsnap's literature database.
Enables searching and retrieving academic publications from HAL (Hyper Article en Ligne) via Patsnap's literature database.
Enables searching and retrieving biomedical literature from PubMed via Patsnap's literature database.
Enables searching and retrieving research papers from SSRN via Patsnap's literature database.
Enables searching and retrieving research outputs from Zenodo via Patsnap's literature database.
Patent & Literature Search MCP
Search global patents and scientific literature in natural language, directly inside Claude, Cursor, or any MCP-compatible AI tool. The MCP supports semantic, keyword, BM25, and structured search, then retrieves selected records as AI-readable Markdown.
Start free: Register on Patsnap Open Platform to receive 10,000 free credits. No credit card is required.
About Patsnap
Patsnap is a global innovation intelligence platform covering patents, scientific literature, chemistry, life sciences, and other R&D data.
To explore Patsnap data interactively, try Eureka, Patsnap's AI-native R&D assistant. To access data programmatically, use the MCP servers or REST API via Patsnap Open Platform.
Related MCP server: patent-mcp
What It Does
Patent & Literature Search MCP gives your AI agent direct access to Patsnap's patent and academic-literature databases. A single natural-language research request can cover both corpora and combine the results for AI reasoning.
Patent search — keyword, assignee, inventor, IPC class, legal status, date range, and citation filters
Literature search — semantic and keyword search across peer-reviewed scientific papers
Cross-domain research — orchestrate patent and paper searches using the same tool and combine the structured results for AI reasoning
Natural language input — describe your problem or topic in plain English or Chinese; the MCP constructs the search strategy
Precision filtering — filter by assignee, inventor, legal status, jurisdiction, date range, and citation count simultaneously
Data Coverage
This MCP is backed by Patsnap's global patent and scientific-literature coverage.
Patent coverage
Data-source group | Representative sources | Coverage |
International and regional authorities | WIPO/PCT, EPO, ARIPO, EAPO, OAPI, GCC | Applications, grants, and regional records |
Major national authorities | USPTO, CNIPA, JPO, KIPO, DPMA, INPI, UKIPO, CIPO, IP Australia | Applications, grants, designs, and utility models where applicable |
Additional jurisdictions | Patent authorities across Europe, Asia-Pacific, the Americas, the Middle East, and Africa | Bibliographic records and authority-supported document types |
Patent data coverage includes:
Data type | What it covers |
Bibliography | Publication and application identifiers, titles, dates, applicants, assignees, inventors, and classifications |
Full text | Abstracts, claims, descriptions, and other document text |
Original patent-document coverage for supported authorities | |
Legal events | Status changes and patent lifecycle events |
File wrapper | Available prosecution and examination records |
License | Available patent licensing records |
Litigation | Available patent litigation records |
Coverage is refreshed frequently, with patent data updated daily.
Scientific-literature coverage
Source type | Representative data sources and publications |
Biomedical and clinical literature | PubMed, The Lancet, BMJ, JAMA, Blood, Cancer Research, Journal of Clinical Oncology, Gastroenterology |
Multidisciplinary journals | Nature, Science, Scientific Reports, PLOS ONE, Proceedings of the National Academy of Sciences |
Chemistry and materials science | Journal of the American Chemical Society, Angewandte Chemie, Journal of Chemical Physics, Chemical & Engineering News Archive |
Physics and engineering | Physical Review Letters, Physical Review B, Journal of Applied Physics, AIP Conference Proceedings, SPIE Proceedings |
Preprints and repositories | arXiv, HAL, SSRN, Zenodo, DOAJ |
Research datasets and specialist sources | ENCODE Datasets, Worldwide Protein Data Bank, OSTI, RePEc, GBIF, PsycEXTRA |
Literature records include bibliographic metadata, abstracts, citation metadata, and PDF coverage. The collection spans journal articles, preprints, conference proceedings, repositories, datasets, and other non-patent literature.
See Patsnap Global Data Coverage for the current authority-level and publication-level coverage matrix.
What You Can Search
patsnap_search searches either patents or scientific literature in one
call. Its current input supports:
Input | What it does |
| Searches a natural-language technical description or concept |
| Combines |
| Runs precise keyword and BM25 matching |
| Selects the |
| Applies provider-supported structured filters |
| Sorts by relevance or a provider-supported field |
| Paginates results; up to 100 results per call |
Patent filters may include assignees, inventors, IPC classifications, jurisdictions, filing or publication dates, legal status, and citation conditions. Exact filter names and operators depend on the current hosted tool schema.
What You Can Retrieve
patsnap_fetch retrieves up to 100 selected result URLs or patent
publication numbers as Markdown.
Patent modules
Module | Content that may be returned |
| Bibliographic data, title, abstract, claims, description, and other available record content |
| Patent citations and non-patent literature references |
| Available patent legal data and events |
| Available patent-family information |
| Signed patent-drawing URLs when available |
Citation data
The citation module can retrieve citation data for selected patents. Where
available, this may include:
patents cited by the selected patent;
patents citing the selected patent;
applicant, examiner, or search-authority citations;
citation categories such as X, Y, and A;
cited non-patent literature; and
DOI and other bibliographic metadata for cited literature.
Citation direction and type should be determined from the returned record labels. Availability and completeness vary by jurisdiction and patent.
Literature records
Literature records support the basic module and may include title,
abstract metadata, authors, affiliations, publication, publication date,
DOI, and other available bibliographic identifiers.
Quick Start
1. Get your API key
Register at Patsnap Open Platform and generate a free API key from the Patent & Literature Search MCP page. New accounts include 10,000 free credits. No credit card required.
2. Add to Claude Code
claude mcp add --transport http search-tool \
"https://connect.patsnap.com/2b0355/logic-mcp?apikey=YOUR_API_KEY"Restart Claude Code and type /mcp to confirm it loaded.
3. Add to Cursor or Windsurf
Add the following to your MCP configuration file:
{
"mcpServers": {
"patsnap_patent_literature": {
"url": "https://connect.patsnap.com/2b0355/logic-mcp?apikey=YOUR_API_KEY",
"type": "streamableHttp"
}
}
}Replace YOUR_API_KEY with your key from Patsnap Open Platform.
Run the Containerized MCP Bridge
The repository includes a local stdio bridge for environments that
install or deploy MCP servers from source. It exposes static tool
definitions without credentials and forwards tool calls to the hosted
Patsnap Streamable HTTP service when PATSNAP_API_KEY is configured.
Docker
docker build -t patsnap-patent-literature-search .
docker run --rm -i \
-e PATSNAP_API_KEY=YOUR_API_KEY \
patsnap-patent-literature-searchMCP client configuration:
{
"mcpServers": {
"patsnap_patent_literature": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"-e",
"PATSNAP_API_KEY",
"ghcr.io/patsnap/patent-literature-search-mcp:latest"
],
"env": {
"PATSNAP_API_KEY": "YOUR_API_KEY"
}
}
}
}The GitHub Actions workflow publishes version tags such as v1.1.0
to GHCR as both 1.1.0 and latest.
An optional live upstream check can be run manually from GitHub
Actions. Add PATSNAP_TEST_API_KEY as a repository Actions secret,
start the workflow with Run workflow, and enable
run_upstream. Pull requests never receive or use this secret.
Tools
patsnap_search— Search either patents or scientific literature in a request. Supports natural-language and semantic queries, precise keyword and BM25-based text search, and filters such as assignee, inventor, IPC classification, legal status, jurisdiction, date range, and citations. A single research request may invoke patent and literature searches and combine their results.patsnap_fetch— Retrieve a patent or literature record as Markdown using one or more result URLs. Patent records can also be fetched by publication number and may include bibliographic data, claims, descriptions, drawings, citations, legal data, and patent-family data. Literature records include core bibliographic and abstract metadata.
Development
Requires Node.js 20 or newer.
npm ci
npm test
npm startThe test suite verifies:
static tool discovery without an API key;
argument validation and upstream forwarding;
structured MCP error handling;
a real stdio
initializeandtools/listhandshake.
To verify the container locally:
docker build -t patent-literature-search-mcp:test .
npm run test:dockerThe server writes MCP JSON-RPC only to stdout. Application errors are returned as MCP tool results, and credentials are never included in logs.
Glama Release
After claiming the server on Glama:
Open the server's Dockerfile Admin page.
Select the repository-root
Dockerfile.Add
PATSNAP_API_KEYas a secret deployment environment variable.Use
node src/index.jsas the command if Glama does not infer the DockerfileCMD.Run Deploy, verify the MCP build test, and create a release.
Example Prompts
What is Tesla actually working on in battery technology — show me
their recent patent filings, group by technology area, and identify
who is leading each.Which GLP-1 receptor agonist patents are expiring before 2028,
and what does recent literature say about next-generation mechanisms?My EV battery loses 40% range in cold weather. What technical
solutions exist in patents and academic papers?Resources
License
Apache-2.0
Powered by Patsnap. Innovate with Confidence.
Available Tools
2 toolspatsnap_fetchFetch PatSnap patent or literature recordsARead-onlyIdempotent
Retrieve Markdown for PatSnap patent or scientific-literature records using result URLs, or fetch patents by publication number. Patent output may include bibliographic data, claims, descriptions, legal data, families, citations, and drawings.
| Name | Required | Description | Default |
|---|---|---|---|
| keys | Yes | Result URLs or patent publication numbers to fetch, up to 100 entries. | |
| module | No | Content modules to retrieve. Patent records support basic, citation, legal, and family; papers support basic. | |
| compress | No | Return gzip-compressed, base64-encoded Markdown to reduce response size. | |
| key_type | No | Identifier type: url for result URLs or pn for patent publication numbers. | |
| include_images | No | Include signed patent drawing URLs when available. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive. Description adds value by detailing potential output contents (bibliographic data, claims, etc.) and explaining compression behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences conveying core functionality and key output details without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description covers what the Markdown may contain and module options. Could be more complete about return format structure, but adequate for a fetch tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but description adds contextual meaning beyond schema, such as explaining which modules apply to patents vs. papers and the purpose of compression.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool retrieves Markdown for PatSnap patent/literature records using result URLs or publication numbers, distinguishing it from the sibling tool patsnap_search which presumably searches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implicitly guides use when you have specific URLs or publication numbers, but lacks explicit when-not-to-use guidance or direct comparison with the search sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
patsnap_searchSearch PatSnap patents and scientific literatureARead-onlyIdempotent
Search PatSnap's global patent or scientific-literature database using semantic concepts, BM25 keywords, and multidimensional filters. Search one corpus per call and preserve returned record URLs for patsnap_fetch.
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | Sort order. Use relevance or a provider-supported field; prefix a field with - for descending order. | |
| limit | No | Number of results to return, from 1 to 100. | |
| offset | No | Number of results to skip. offset + limit must not exceed 1000. | |
| source | No | Single corpus to search. Use patent for global patent data or paper for scientific literature. | |
| filters | No | Structured filters such as assignees, inventors, IPC classifications, jurisdictions, dates, legal status, or citations. | |
| keywords | No | Technical terms used for BM25 keyword matching. | |
| semantic_query | No | Natural-language description of the technical problem or concept to search. | |
| search_strategy | No | Search strategies to combine: semantic search, BM25 keyword matching, and structured filtering. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and open-world semantics. The description adds a behavioral constraint: 'Search one corpus per call' and hints at preserving returned URLs for a follow-up tool. No contradictions with annotations. The added detail is valuable but not extensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences, front-loaded with the main action, and under 50 words. Every sentence adds value—purpose, corpus constraint, and integration with the sibling tool. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (8 parameters, 1 nested object, no output schema), the description covers core functionality and a key constraint but omits details about the return format or structure. The instruction to 'preserve returned record URLs' hints at the output but does not explain what the response contains. For a search tool, this is adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description uses terms like 'semantic concepts, BM25 keywords, and multidimensional filters' that map to parameters but does not add meaning beyond the schema's own descriptions. No new insight into parameter usage is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: searching PatSnap's global patent or scientific literature database using semantic concepts, BM25 keywords, and filters. It explicitly mentions the ability to search one corpus per call and distinguishes from the sibling tool patsnap_fetch by instructing to preserve returned URLs for subsequent fetching.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides guidance on when to use the tool: for initial searches, and instructs to preserve record URLs for the sibling patsnap_fetch. It mentions searching one corpus per call, which helps avoid misuse. However, it does not explicitly state when not to use this tool or describe alternatives beyond the sibling, but the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
The two tools have clearly distinct purposes: one for searching the database and one for fetching detailed records. There is no overlap or ambiguity.
Both tools follow a consistent 'patsnap_verb' pattern with clear, descriptive verbs ('search' and 'fetch'), making it easy to understand their functions.
With only 2 tools, the server is minimal but focused on a specific workflow (search then fetch). This is reasonable, though additional tools for managing searches or filters could be added.
The server covers the core tasks of searching and retrieving patent/literature records. It lacks features like listing recent searches or browsing categories, but the essential lifecycle is present.
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