General
mpep-search - Claude MCP Skill
Expert system for searching USPTO MPEP, 35 USC statutes, 37 CFR regulations, and post-Jan 2024 updates.
SEO Guide: Enhance your AI agent with the mpep-search tool. This Model Context Protocol (MCP) server allows Claude Desktop and other LLMs to expert system for searching uspto mpep, 35 usc statutes, 37 cfr regulations, and post-jan 2024 updat... Download and configure this skill to unlock new capabilities for your AI workflow.
Documentation
SKILL.md# MPEP Search Skill
Search MPEP corpus through hybrid RAG (FAISS vector + BM25 keyword + HyDE + cross-encoder reranking).
**Sources:**
- MPEP: Manual of Patent Examining Procedure
- 35 USC: United States Code Title 35
- 37 CFR: Code of Federal Regulations Title 37
- Subsequent Publications: Federal Register updates (post-Jan 2024)
## Core Operations
### 1. `search_mpep`
**Inputs:**
- `query` (string, required): Search query (minimum 3 characters)
- `top_k` (int, optional): Number of results (default: 5, max: 20)
- `retrieve_k` (int | None, optional): Candidates before reranking (default: top_k * 4, max: 100)
- `source_filter` (string | None, optional): Filter by source (`"MPEP"`, `"35_USC"`, `"37_CFR"`, `"SUBSEQUENT"`, or `None`)
- `is_statute` (bool | None, optional): Filter for statute content
- `is_regulation` (bool | None, optional): Filter for regulation content
- `is_update` (bool | None, optional): Filter for recent updates
**Outputs:**
```python
{
"rank": int,
"source": str,
"section": str,
"file": str,
"page": int,
"has_statute": bool,
"has_mpep_ref": bool,
"has_rule_ref": bool,
"is_statute": bool,
"is_regulation": bool,
"is_update": bool,
"relevance_score": float,
"text": str,
# Optional for SUBSEQUENT:
"doc_type": str,
"fr_citation": str,
"effective_date": str
}
```
**Examples:**
```python
# Basic search
search_mpep("enablement requirement 35 USC 112", top_k=5)
# Search only statutes
search_mpep("written description", top_k=10, is_statute=True)
# Search recent updates
search_mpep("AI inventorship", is_update=True)
# Filter by source
search_mpep("fee schedule", source_filter="37_CFR")
```
### 2. `get_mpep_section`
Retrieve all content from specific MPEP section.
**Inputs:**
- `section_number` (string, required): MPEP section number (e.g., `"2100"`, `"608.01"`)
- `max_chunks` (int, optional): Maximum chunks to return (default: 50)
**Outputs:**
```python
{
"section": str,
"total_chunks": int,
"chunks": [
{
"text": str,
"metadata": {
"source": str,
"file": str,
"page": int,
"section": str,
"has_statute": bool,
"has_mpep_ref": bool,
"has_rule_ref": bool,
"is_statute": bool,
"is_regulation": bool,
"is_update": bool
}
}
]
}
```
**Error Response:**
```python
{"error": "No content found for MPEP section {section_number}"}
```
**Examples:**
```python
# Get MPEP 2100 (Patentability)
get_mpep_section("2100", max_chunks=50)
# Get subsection
get_mpep_section("608.01")
```
## Input Validation
**Query validation:**
- Minimum 3 characters
- Case-insensitive
- No empty/whitespace-only queries
**Section number validation:**
- Numeric with optional decimal (e.g., "100", "2100", "608.01")
**Limits:**
- `top_k` capped at 20
- `retrieve_k` capped at 100
## Implementation Notes
**Index Location:**
- FAISS index: `mcp_server/index/mpep_index.faiss`
- Metadata: `mcp_server/index/mpep_metadata.json`
- BM25 index: `mcp_server/index/mpep_bm25.json`
**Search Architecture:**
1. HyDE Query Expansion (hypothetical documents)
2. Hybrid Retrieval (FAISS vector + BM25 keyword via RRF)
3. Cross-Encoder Reranking (final relevance scores)
4. Metadata Filtering (source/type filters)
**Dependencies:**
- sentence-transformers (BGE-base-en-v1.5)
- FAISS (vector search)
- rank-bm25 (keyword search)
- Cross-encoder (reranking)
- HyDE (optional, graceful degradation)
**Error Handling:**
- Clear error messages for missing index/invalid queries
- Graceful degradation if HyDE fails
- Input validation before processingSignals
Information
- Repository
- RobThePCGuy/Claude-Patent-Creator
- Author
- RobThePCGuy
- Last Sync
- 9/5/2026
- Repo Updated
- 9/4/2026
- Created
- 3/26/2026
Reviews (0)
No reviews yet. Be the first to review this skill!
Related Skills
cursorrules
CrewAI Development Rules
README
Agents — Working Implementations
firecrawl-build-search
Integrate Firecrawl `/search` into product code and agent workflows. Use when an app needs discovery before extraction, when the feature starts with a query instead of a URL, or when the system should search the web and optionally hydrate result content.
firecrawl-build-onboarding
Get Firecrawl credentials and SDK setup into a project. Use when an application needs `FIRECRAWL_API_KEY`, when an agent should add Firecrawl to `.env`, when the user wants to authenticate Firecrawl for app code, or when choosing the first SDK and docs for a new Firecrawl integration. This skill includes its own browser auth flow, so it does not depend on the website onboarding skill.
Related Guides
Python Django Best Practices: A Comprehensive Guide to the Claude Skill
Learn how to use the python django best practices Claude skill. Complete guide with installation instructions and examples.
Mastering Python and TypeScript Development with the Claude Skill Guide
Learn how to use the python typescript guide Claude skill. Complete guide with installation instructions and examples.
Mastering Data Science with Claude: A Complete Guide to the Pandas Scikit-Learn Skill
Learn how to use the pandas scikit learn guide Claude skill. Complete guide with installation instructions and examples.