About Patent Mcp Server
Patent Mcp Server is an MCP server for AI agents that retrieve and analyze patent records. It combines patent details and claims from Google Patents with optional BigQuery search and a fallback for some Chinese CPC searches.
Key features
- 01Retrieve patent details and citations
- 02Read claims across supported countries
- 03Search by keyword, CPC, assignee, country, or date
- 04Chinese keyword search across English and Chinese abstracts
- 05Optional BigQuery search with query budget checks
Use cases
- 01Review claims and prior-art citations for a patent
- 02Map patents by company or CPC class
- 03Search Chinese patents using keywords or assignees
MCP support
Exposes patent detail, claims, and search tools to MCP clients. Web-backed lookups work without BigQuery credentials; search can optionally use BigQuery and a Chinese-query fallback.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
11Saved tool list · updated 3w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| cpc | string | No | CPC classification code prefix, e.g. 'G06F40', 'H01L' |
| after | string | No | Earliest filing date, format YYYY-MM-DD |
| limit | integer | No | Maximum results (default 10, max 50) |
| query | string | No | Optional keyword/technology area/inventor name to search |
| before | string | No | Latest filing date, format YYYY-MM-DD |
| status | enum | No | Patent status filtergrant | application |
| country | string | No | Country code filter, e.g. 'CN', 'US', 'EP' |
| assignee | string | No | Optional assignee/organization name filter (fuzzy match on harmonized names). Use for company-level or city-level analysis, e.g. 'HEFEI', 'BOE', 'HUAWEI'. |
Details
From the project READMEPatent MCP Server
🚀 中国专利最准确的开源 MCP。 Give your AI agent the ability to read CN patents with real accuracy — plus global coverage.
An MCP (Model Context Protocol) server that gives AI agents access to patent data — CN patents with CPC-aware correction, plus US/WO global coverage. Runs locally on your machine. No external API, no subscription. Always MIT.
Why Self-Deployed
- It's just Python. Install it, your agent uses it. No server to maintain, no credential to share.
- No API key for 80% of use cases. Patent details and claims come straight from Google Patents public pages.
- Your data stays local. Nothing leaves your machine except the same HTTP requests a browser would make.
- BigQuery search is optional. Only turn it on if you need full-text search across 1.4B records.
30-Second Install
pip install deeparchi-patent-mcp
Or from source:
git clone https://github.com/deeparchi-ai/patent-mcp-server.git
cd patent-mcp-server
pip install -e .
Quick Start
After pip install, the deeparchi-patent-mcp console script is on your PATH.
Add this to your agent platform's MCP config:
Claude Desktop
{
"mcpServers": {
"patent-mcp": {
"command": "deeparchi-patent-mcp",
"args": []
}
}
}
Cursor / Windsurf / Cline
Same config as Claude Desktop above.
Hermes Agent
mcp_servers:
patent-mcp:
command: "deeparchi-patent-mcp"
BigQuery is optional. Without
GCP_PROJECT_IDthe server starts normally and the web-backed tools work with no credentials. BigQuery-backed tools return a message explaining how to enable them. To turn them on, add an env var:{ "mcpServers": { "patent-mcp": { "command": "deeparchi-patent-mcp", "args": [], "env": { "GCP_PROJECT_ID": "your-gcp-project" } } } }
Running from a source checkout
{
"mcpServers": {
"patent-mcp": {
"command": "python",
"args": ["-m", "server"],
"cwd": "/path/to/patent-mcp-server/src"
}
}
}
Now ask your agent:
"Get patent US-7650331-B1 and summarize the claims."
What's Included
| Tool | What It Does | Needs Setup? |
|---|---|---|
get_patent | Full patent details: classifications, citations (X/Y/A/D), inventors, assignees, family | No |
get_patent_claims | Patent claims text — legal scope. Supports US, CN, and most countries via Google Patents | No |
search_patents | Search 1.4B patents by keyword, CPC, assignee, country, date range | Optional GCP |
The first two cover 80% of use cases. Zero cost. Zero setup.
CN Patent Search (v1.7.0)
Three-layer discovery for Chinese patents:
| Layer | Backend | Cost | When |
|---|---|---|---|
| 1. BigQuery | Google Patents Public Data | Free tier | cpc=H01L + country=CN |
| 2. Firecrawl | Web search fallback | 4 credits/query | BigQuery cost-rejects specific CPC (e.g. H01L25/065) |
| 3. Google Patents | Detail enrichment via proxy | Free | All patent detail lookups |
- Keyword search works:
query="芯片" + country=CNsearches both English AND Chinese abstracts (v1.5.2 fix). - Assignee filter:
assignee="BOE" + country=CN→ company/city-level patent landscape. - CPC classification: Use parent CPC classes (
H01L) for broader CN coverage; specific CPC codes (H01L25/065) trigger web fallback. - See
SEARCH_GUIDE.mdfor detailed search strategy and tested CPC codes.
Optional: Enable BigQuery Search
If you need search_patents, add a GCP project:
- Create a GCP project with BigQuery enabled
- Create a service account, download JSON key
- Set env vars:
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/key.json" export GCP_PROJECT_ID="your-project-id" - Copy the wrapper template and fill in your paths:
cp run.sh.example run.sh # Edit run.sh → set your GCP paths
BigQuery free tier: 1 TB/month — individual use is essentially free.
Advanced: Team Server (HTTP/SSE)
Need multiple people to share one patent-mcp instance? Start it as an HTTP server:
cp run-http.sh.example run-http.sh
# Edit → set GCP creds (skip if only using web tools)
PORT=8090 ./run-http.sh
Team members connect with:
mcp_servers:
patent-mcp:
url: "http://<server-ip>:8090/sse"
A systemd service template is included for production deployment.
How It Works
┌──────────────┐ ┌───────────────────────────────────────┐
│ AI Agent │────▶│ patent-mcp-server │
│ (Claude, │ │ (runs on YOUR machine) │
│ Cursor, │ │ │
│ Hermes) │ │ search_patents: │
│ │ │ ┌──────────┐ ┌───────────────┐ │
│ │ │ │ BigQuery │───▶│ Firecrawl │ │
│ │ │ │ (primary)│ │ (CN fallback) │ │
│ │ │ └──────────┘ └───────┬───────┘ │
│ │ │ │ │
│ │ │ get_patent / get_patent_claims: │
│ │ │ ┌──────────────────────┐ │
│ │ │ │ Google Patents (web) │ │
│ │ │ │ → BigQuery fallback │ │
│ │ │ └──────────────────────┘ │
└──────────────┘ └───────────────────────────────────────┘
- Web scraping for details — fast (~1.5s), free, no credentials
- BigQuery for search — 1.4B records, CN full-text, optional
- Firecrawl for CN fallback — kicks in when BigQuery cost-rejects specific CPC queries
- Smart fallback — every tool tries web first, auto-falls to BigQuery if you have it
Tools Reference
get_patent
get_patent(publication_number="US-7650331-B1")
Returns: classifications, citations (X/Y/A/D prior art markers), family ID, dates, inventors, assignees. Cites prior art markers so your agent can assess novelty at a glance.
CN patent note: Google Patents web scraping provides machine-translated English data for CN patents. CPC codes from web scraping are empty (JS-rendered). For CPC, use BigQuery path.
get_patent_claims
get_patent_claims(publication_number="US-7650331-B1")
Returns: full claims text. Supports US, CN (machine-translated English), and most countries via Google Patents web scraping.
search_patents
search_patents(cpc="G06N", country="CN", after="2023-01-01", limit=5)
search_patents(assignee="TSMC", country="CN")
search_patents(query="芯片", country="CN") # keyword search (CN: searches both EN+ZH)
Search 1.4B patents by keyword, CPC classification, assignee, country, date range. For CN patents, keyword search scans both English and Chinese abstracts.
Cost control: Queries require at least one filter (cpc/country/assignee/after). A dry-run budget guard rejects queries over 50 GB. When BigQuery rejects a CN CPC query (e.g., H01L25/065 → 256 GB), the web fallback automatically searches via Firecrawl.
See SEARCH_GUIDE.md for best practices and docs/cn-cpc-correction-table.md for tested CPC codes.
Development
pip install -e ".[dev]"
pytest tests/ -v # 32 tests, ~1.5s
ruff check src/ tests/ # lint
mypy src/ # type check
License
MIT — see LICENSE.
Author
DeepArchi OPC — AI agent infrastructure for enterprise architecture.
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