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name: patent-search description: Search 100M+ patents via the MCP server's BigQuery tools. No standalone scripts; everything goes through the MCP tools registered by the patent-creator server.

Patent Search Skill

This skill points Claude at the BigQuery patent-search tools registered by the patent-creator MCP server. Call the tools directly; do not shell out to Python.

When to use

• Find prior art by keyword, classification, or family.
• Pull full patent records (title, abstract, claims, description) for US patents.
• Cross-reference an EP/WO patent into its US family member to get full text.

Available MCP tools

Tool	What it does
search_patents_google	Recommended. Full-text keyword search worldwide (claims and description included), one search credit per call. Needs SERPAPI_API_KEY (free plan: 250/month).
check_google_patents_status	Is the key set, and how many searches are left this month (free to call).
search_patents_bigquery	Fallback keyword search across title / abstract / claims (US only for claims). ~341 GiB, about $2, per search.
get_patent_bigquery	Patent details by publication number: bibliographic data + claims by default; abstract and description are opt-in.
get_patents_bigquery	Same, for up to 50 patents in one query at the cost of one lookup.
search_patents_by_cpc_bigquery	Search by CPC classification prefix.
search_patents_by_ipc_bigquery	Search by IPC classification prefix (good for older or non-US patents).
search_patent_family_bigquery	All publications sharing a family ID across jurisdictions.
check_bigquery_status	Verify auth and quota project before a long workflow.

Cost notes

BigQuery on-demand pricing is $6.25 / TiB (1 TiB free per month). The MCP server enforces a per-query bytes-billed ceiling, defaulting to 350 GiB. Override via PATENT_BIGQUERY_MAX_BYTES_BILLED if you need a larger scan window.

The patents table is unclustered, so a detail lookup costs the same for one patent or fifty; the cost depends only on which sections are requested:

Detail lookup	Scan	Cost
Bibliographic only (include_claims=False): title, dates, family_id, CPC/IPC	~44 GiB	~$0.27
Default: + claims	~160 GiB	~$1
+ abstract (include_abstract=True)	+~200 GiB	exceeds the default cap together with claims
+ description (include_description=True)	~1.1 TiB	exceeds the default cap

So: batch with get_patents_bigquery, pass include_claims=False when you only need codes or family_id, and take abstracts from the search results rather than re-fetching them.

Choosing keywords

search_patents_google:

• All words must match; "double quotes" match an exact phrase. No stemming, so search synonyms and plurals separately.
• Lead with the invention's distinctive terms. Common words match thousands of patents and push the closest art down; in testing, adding "heart valve" to a query buried the target past #300, while its distinctive claim words found it at #48.
• Use limit=100: it costs the same one credit as limit=10.
• Stay inside a classification by adding a full CPC group: "voice biometric" CPC=G10L17/00 (a bare CPC=G10L17 finds nothing).
• Results show one row per publication, so a US patent and its EP family member can both appear.

search_patents_bigquery (fallback):

• 2-3 keywords work better than long phrases (BigQuery LIKE matching is literal).
• For non-US patents, claims is empty in the dataset; the MCP keyword tool already searches title/abstract for those jurisdictions. For full text on EP/WO, use the EPO OPS tools instead.
• Use search_patent_family_bigquery to bridge from an EP/WO hit to its US family member when you need claims.

Common workflows

Prior art sweep:

1. search_patents_google(query=…, limit=100) — broad full-text scan, worldwide (fallback: search_patents_bigquery(query=…, country="US")).
2. Pick the top hits' CPC codes with one get_patents_bigquery(patent_numbers=[…], include_claims=False).
3. search_patents_google(query="<terms> CPC=<group>/00", limit=100) — pull adjacent technology (fallback: search_patents_by_cpc_bigquery, ~234 GiB a search).

Cross-jurisdiction lookup:

1. search_patents_google(query=…, country="EP") (fallback: search_patents_bigquery(query=…, country="EP")).
2. For the EP hits, one get_patents_bigquery(patent_numbers=[…], include_claims=False) to get their family_ids.
3. search_patent_family_bigquery(family_id=…) to find the US member with full claims.