Patsnap Patent Paper Search

By Patsnap

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Overview

A professional tool enabling deep, integrated search across the Patsnap global patent and literature databases simultaneously. It supports natural language queries, semantic search, and keyword search, backed by precise pre-filtering capabilities for efficient and accurate retrieval of scientific and patent intelligence. This MCP also allows users to fetch basic bibliographic data for patents or literature in Markdown format.

Tools

patsnap_fetch

ChatGPT

Patent or Paper Markdown Fetch Tool Tool Description This is a data retrieval tool designed for AI Agents to fetch patent and paper data. It supports batch retrieval of Markdown-formatted content based on a list of keys (URLs containing patentId or paperId, or patent publication numbers), enabling AI to perform content analysis, reasoning, and Q&A. Core Features - Batch Query: Supports querying one or multiple patents or papers at once (up to 100) - Auto Detection: Automatically identifies whether the input is a patent URL (containing patentId), a paper URL (containing paperId), or a patent publication number (pn) - Format Conversion: Converts structured data into readable Markdown format - Image Handling: Optionally includes image links (for patents) - Content Compression: Optionally compresses returned content to reduce transfer size - Module Selection: Optionally specify which data modules to retrieve (basic, citations, legal, family) - Structured Data: Optionally returns the raw structured JSON data alongside Markdown, grouped by module Input Parameters keys (required) - Type: Array of strings - Description: List of keys (URLs containing patentId or paperId, or patent publication numbers) - Patent URL format: e.g. https://eureka.zhihuiya.com/view/#/fullText'figures/?patentId=07c38692-9ccf-4ead-8f0f-484f7aa929b7 - Paper URL format: e.g. https://eureka.zhihuiya.com/literature/#/abst?paperId=c53a8bcb-b7a3-42c9-8a6f-a26d6b5dec9a - Patent publication number (pn) format: e.g. US20240368139A1 - Count limit: 1–100 (10–20 recommended for best performance) key_type (optional) - Type: String - Default: "url" - Description: Key type — either a URL (containing patentId or paperId) or a patent publication number (pn) - Usage: Set to "url" when the input keys are URLs; set to "pn" when the input keys are patent publication numbers - Allowed values: url, pn compress (optional) - Type: Boolean - Default: false - Description: Whether to compress the returned Markdown content (using gzip + base64 encoding) - Usage: Recommended when the returned content is large include_images (optional) - Type: Boolean - Default: false - Description: Whether to include image links (only applicable to patents) - Note: When enabled, image URLs will be automatically signed module (optional) - Type: Array of strings - Default: ["basic"] - Description: List of data modules to retrieve (only applicable to patents) - Allowed values: - basic — Core patent info (biblio, title, abstract, claims, description, etc.) - citation — Citation and cited-by records - legal — Legal status, assignment, license records - family — Patent family members (original, INPADOC, extended) - Note: citation, legal, and family are only supported for patents, not papers - Examples: ["basic"], ["basic", "citation"], ["basic", "citation", "legal", "family"] include_structured (optional) - Type: Boolean - Default: false - Description: Whether to return the raw structured JSON data (the structured field) alongside the Markdown content - Usage: Enable when you need the original source data for further processing Output Successful Response Returns a JSON object with the following fields: - total: Total number of keys submitted - success_count: Number of successfully converted items - failed_count: Number of failed items - results: List of results, each containing: - key: The original input key (URL or patent publication number) - key_type: The type of the original input key (url or pn) - markdown: The converted Markdown content - images: List of image info (only present for patents when include_images is enabled) - success: Whether the conversion was successful - message: Processing message - source: Data source (patent or paper) - modules: The list of modules queried for this item - structured: Raw structured JSON data grouped …

patsnap_search

ChatGPT

Patent / Paper Search Search one source per request (source: patent default, or paper). Returns a ranked page of matching documents with metadata and snippets. Use for Finding or listing patents/papers by topic, technical problem, company/applicant, author/inventor, identifier, classification, jurisdiction, date range, legal status, or citation threshold — for review, comparison, or ranking. Do NOT use for - Counts, trends, distributions, top-N, or citation statistics — this tool returns documents, not aggregates. - Full document content (full text, claims, descriptions, images, family, citation lists, legal detail) or raw source records. - Chit-chat or general knowledge unrelated to patents/papers. Invariants (MUST obey) 1. Strategy ⇄ params binding is strict. - "semantic" in search_strategy → MUST provide semantic_query; if "semantic" is NOT in search_strategy → MUST NOT provide semantic_query. - "keyword" in search_strategy → MUST provide keywords; if "keyword" is NOT in search_strategy → MUST NOT provide keywords. - "filter" in search_strategy → MUST provide filters with at least one field set; if "filter" is NOT in search_strategy → MUST NOT provide filters. 2. `semantic_query` describes the technical topic or search intent in natural language. - Use it for semantic retrieval of technical topics, questions, or mechanisms. For patents, also use it for target languages unsupported by keyword search. - Structured constraints such as companies, identifiers, dates, and jurisdictions belong in filters. 3. `keywords` are atomic topic terms for BM25 text matching. - Each item should be one technical term, product/standard name, abbreviation, method, material, or research concept. - Keyword search supports Chinese, English, and Japanese. Use terms in the requested target languages; for patents, use all three when no target language is specified. Use semantic search for other patent target languages. - Do not pass sentences, comma-joined strings, or generic filler such as "technology", "method", "system", "development". - Do not put applicant/company/assignee, inventor, author, or organization names in keywords; those belong in filters. 4. `filters` are structured constraints, not text keywords. - Use filters for company/applicant/assignee, inventor/author, organization, identifier, jurisdiction, IPC, legal status, date range, and citation threshold constraints. - Only set fields the user explicitly states. Do not infer dates, jurisdiction, IPC, assignees, inventors, authors, organizations, legal status, or citation thresholds. - filters must be a JSON object, never a stringified/escaped JSON string. 5. Pagination limits are fixed. - limit 1–100, offset ≥ 0, offset + limit ≤ 1000. Filter fields - source="patent": pn, apno, assignees, assignee_ids, inventors, jurisdiction, ipc, legal_status, date_from, date_to, date_type, cited_min - source="paper": authors, org_names, date_from, date_to, cited_min Examples ``

json {"source":"paper","search_strategy":["semantic"],"semantic_query":"pharmaceutical technical solutions for treating glaucoma","limit":20}

` `

json {"source":"patent","search_strategy":["keyword","filter"],"keywords":["5G","天线","antenna","アンテナ","MIMO"],"filters":{"assignees":["Huawei"],"jurisdiction":["CN","US"]},"limit":50}

` `

json {"source":"paper","search_strategy":["filter"],"filters":{"authors":["Zhang San"],"date_from":20230101,"date_to":20241231},"limit":30}

patsnap_fetch

Claude

patsnap_search

Claude

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