fix: resolve remaining merge conflicts across all affected files

MCP_SETUP.md

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 # 🏥 NurseLex MCP Server Setup Guide
 
 Welcome to the **NurseLex MCP Server**! This server uses the **Model Context Protocol (MCP)** to inject the entire i.AI Lex UK legislative database directly into your favorite AI Assistant (like Claude Desktop or Cursor).
@@ -66,72 +65,3 @@ If you are a Nurse Citizen Developer building applications in Cursor, you can ad
 5. Click **Save** and verify the connection is green. 
 
 You can now ask Cursor Agent: *"Write a Python function that uses NurseLex to find the legal requirements for Section 136 of the MHA."*
-=======
-# 🏥 NurseLex MCP Server Setup Guide
-
-Welcome to the **NurseLex MCP Server**! This server uses the **Model Context Protocol (MCP)** to inject the entire i.AI Lex UK legislative database directly into your favorite AI Assistant (like Claude Desktop or Cursor).
-
-When connected, your AI assistant gains three powerful real-time tools:
-
-1. **`search_local_nursing_cache`**: Instantly retrieves the exact statutory text for 1,128 critical nursing sections (Mental Health Act, Care Act, etc.).
-2. **`get_official_explanatory_note`**: Fetches the official UK Government plain English explainer for a specific Act and section.
-3. **`vector_search_lex_api`**: A semantic search engine that maps clinical, plain-English scenarios (e.g., "patient lacks capacity to consent to treatment") to relevant national laws.
-
----
-
-## 🛠️ Prerequisites
-
-1. You must have **Python 3.10+** installed.
-2. Install the required dependencies:
-   ```bash
-   pip install mcp fastmcp httpx pandas
-   ```
-
----
-
-## 🔌 Connecting to Claude Desktop
-
-To let Claude Desktop use NurseLex as a knowledge tool, you need to edit your `claude_desktop_config.json` file.
-
-### 1. Locate your Config File
-- **Windows:** `%APPDATA%\Claude\claude_desktop_config.json`
-- **Mac:** `~/Library/Application Support/Claude/claude_desktop_config.json`
-
-### 2. Add the NurseLex Server
-Open the file in a text editor and add the following configuration to your `mcpServers` block. 
-
-*Make sure to replace `/path/to/NurseLex` with the actual folder path where you downloaded the project!*
-
-```json
-{
-  "mcpServers": {
-    "nurselex": {
-      "command": "python",
-      "args": [
-        "/path/to/NurseLex/mcp_server.py"
-      ]
-    }
-  }
-}
-```
-
-### 3. Restart Claude
-Fully close and restart the Claude Desktop app. You should now see a 🔨 (hammer) icon in the chat bar indicating the tools are loaded!
-
----
-
-## 🖱️ Connecting to Cursor IDE
-
-If you are a Nurse Citizen Developer building applications in Cursor, you can add NurseLex to help write legally compliant code!
-
-1. Open Cursor Settings (**Cmd + Shift + J** on Mac, **Ctrl + Shift + J** on Windows).
-2. Go to the **Features** -> **MCP** section.
-3. Click **+ Add New MCP Server**.
-4. Set the following:
-   - **Name:** `NurseLex`
-   - **Type:** `command`
-   - **Command:** `python /path/to/NurseLex/mcp_server.py` (Use the absolute path to your folder).
-5. Click **Save** and verify the connection is green. 
-
-You can now ask Cursor Agent: *"Write a Python function that uses NurseLex to find the legal requirements for Section 136 of the MHA."*
->>>>>>> a4e257b16d56f80612b7c9ac6d2e7c198fef5bb6

cached_legislation.py

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 """
 NurseLex — Pre-cached legislation for offline lookup.
 Loads full section text from nursing_sections.json (1,000+ sections).
@@ -114,120 +113,3 @@ def search_cached(query: str, max_results: int = 5) -> list:
                 break
                 
     return results[:max_results]
-=======
-"""
-NurseLex — Pre-cached legislation for offline lookup.
-Loads full section text from nursing_sections.json (1,000+ sections).
-Source: legislation.gov.uk (Crown Copyright, OGL v3.0) via i.AI Lex API.
-"""
-import os
-import json
-import logging
-
-logger = logging.getLogger(__name__)
-
-# --- Configuration ---
-JSON_PATH = os.path.join(os.path.dirname(__file__), "nursing_sections.json")
-
-# --- Load Sections ---
-CACHED_SECTIONS = []
-try:
-    if os.path.exists(JSON_PATH):
-        with open(JSON_PATH, "r", encoding="utf-8") as f:
-            CACHED_SECTIONS = json.load(f)
-        logger.info(f"Loaded {len(CACHED_SECTIONS)} sections from {JSON_PATH}")
-    else:
-        logger.warning(f"Metadata file {JSON_PATH} not found.")
-except Exception as e:
-    logger.error(f"Error loading {JSON_PATH}: {e}")
-
-# --- Keyword Map for Natural Language Shortcuts ---
-# Direct section links for common nursing search terms
-KEYWORD_MAP = {
-    "nurse holding power": ("ukpga/1983/20", "5(4)"),
-    "doctor holding power": ("ukpga/1983/20", "5(2)"),
-    "section 2": ("ukpga/1983/20", "2"),
-    "section 3": ("ukpga/1983/20", "3"),
-    "section 4": ("ukpga/1983/20", "4"),
-    "aftercare": ("ukpga/1983/20", "117"),
-    "section 117": ("ukpga/1983/20", "117"),
-    "leave of absence": ("ukpga/1983/20", "17"),
-    "section 17": ("ukpga/1983/20", "17"),
-    "place of safety": ("ukpga/1983/20", "136"),
-    "section 136": ("ukpga/1983/20", "136"),
-    "section 135": ("ukpga/1983/20", "135"),
-    "best interests": ("ukpga/2005/9", "4"),
-    "capacity test": ("ukpga/2005/9", "3"),
-    "functional test": ("ukpga/2005/9", "3"),
-    "mca principles": ("ukpga/2005/9", "1"),
-    "safeguarding": ("ukpga/2014/23", "42"),
-    "section 42": ("ukpga/2014/23", "42"),
-    "advocacy": ("ukpga/2014/23", "67"),
-}
-
-def search_cached(query: str, max_results: int = 5) -> list:
-    """
-    Search local sections by keyword, title, or legislation ID.
-    Returns a list of section dictionaries.
-    """
-    if not query:
-        return []
-    
-    query = query.lower().strip()
-    results = []
-    
-    # 1. Check Keyword Map First (High Precision)
-    for kw, (leg_id, sec_num) in KEYWORD_MAP.items():
-        if kw in query:
-            # Find the specific section in our list
-            for s in CACHED_SECTIONS:
-                if s.get("legislation_id") == leg_id and str(s.get("number")) == sec_num:
-                    if s not in results:
-                        results.append(s)
-            # Find closest matches if exact number not found (e.g. 5(4) vs 5)
-            if not results:
-                 for s in CACHED_SECTIONS:
-                    if s.get("legislation_id") == leg_id and str(s.get("number")).startswith(sec_num.split('(')[0]):
-                        if s not in results:
-                             results.append(s)
-
-    # 2. Text-based Search
-    # Sort sections by relevance (title match > text match)
-    scored_results = []
-    for s in CACHED_SECTIONS:
-        score = 0
-        title = s.get("title", "").lower()
-        text = s.get("text", "").lower()
-        leg_id = s.get("legislation_id", "").lower()
-        num = str(s.get("number", "")).lower()
-        
-        # Exact section reference (e.g. "Section 5")
-        if f"section {num}" in query or f"s.{num}" in query or f"s {num}" in query:
-             score += 100
-             
-        # Title matches
-        if query in title:
-            score += 50
-        
-        # Word matches in title
-        for word in query.split():
-            if len(word) > 3 and word in title:
-                score += 10
-        
-        # Content matches
-        if query in text:
-            score += 5
-            
-        if score > 0:
-            scored_results.append((score, s))
-            
-    # Sort and add to results
-    scored_results.sort(key=lambda x: x[0], reverse=True)
-    for _, s in scored_results:
-        if s not in results:
-            results.append(s)
-            if len(results) >= max_results:
-                break
-                
-    return results[:max_results]
->>>>>>> a4e257b16d56f80612b7c9ac6d2e7c198fef5bb6

lex_client.py

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 """
 NurseLex — Lex API Client
 Wraps the i.AI Lex API for nursing-focused UK legislation search.

mcp_server.py

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 import asyncio
 import httpx
 import pandas as pd
@@ -174,180 +173,3 @@ async def get_official_explanatory_note(act_name: str, section_number: str) -> s
 if __name__ == "__main__":
     # Ensure this runs correctly when started via cursor/claude
     mcp.run()
-=======
-import asyncio
-import httpx
-import pandas as pd
-import json
-import os
-from mcp.server.fastmcp import FastMCP
-
-# Initialize FastMCP server
-mcp = FastMCP("NurseLex-LexAPI")
-
-# Core Constants
-BASE_URL = 'https://lex.lab.i.ai.gov.uk'
-NURSING_ACTS = {
-    "Mental Health Act 1983": "ukpga/1983/20",
-    "Mental Capacity Act 2005": "ukpga/2005/9",
-    "Care Act 2014": "ukpga/2014/23",
-    "Human Rights Act 1998": "ukpga/1998/42",
-    "Equality Act 2010": "ukpga/2010/15",
-    "Health and Social Care Act 2012": "ukpga/2012/7",
-    "Mental Health Units (Use of Force) Act 2018": "ukpga/2018/27",
-    "Autism Act 2009": "ukpga/2009/15",
-    "Children Act 1989": "ukpga/1989/41",
-    "Children Act 2004": "ukpga/2004/31",
-    "Safeguarding Vulnerable Groups Act 2006": "ukpga/2006/47",
-    "Health and Care Act 2022": "ukpga/2022/31",
-}
-REVERSE_ACTS = {v: k for k, v in NURSING_ACTS.items()}
-
-# Load Cache (we need absolute paths since MCP might run from a different CWD)
-DB_DIR = os.path.dirname(os.path.abspath(__file__))
-CACHE_FILE = os.path.join(DB_DIR, "nursing_sections.json")
-
-def _load_sections():
-    try:
-        with open(CACHE_FILE, 'r', encoding='utf-8') as f:
-            return json.load(f)
-    except FileNotFoundError:
-        return []
-
-SECTIONS_CACHE = _load_sections()
-
-@mcp.tool()
-def search_local_nursing_cache(query: str, limit: int = 5) -> str:
-    """
-    Search the local, curated cache of 1,128 critical nursing legislation sections 
-    (from the Mental Health Act, Care Act, etc.) for a specific keyword or section number.
-    Returns the exact statutory text.
-    """
-    if not SECTIONS_CACHE:
-        return "Error: Local cache not found."
-        
-    query_lower = query.lower()
-    results = []
-    
-    for section in SECTIONS_CACHE:
-        act_name = section.get('act_name', '').lower()
-        title = section.get('title', '').lower()
-        text = section.get('text', '').lower()
-        num_str = str(section.get('number', ''))
-        
-        score = 0
-        if query_lower in act_name: score += 1
-        if query_lower in title: score += 3
-        if query_lower in text: score += 1
-        if query_lower == f"section {num_str}" or query_lower == num_str: score += 5
-        
-        if score > 0:
-            results.append((score, section))
-            
-    # Sort and take top matches
-    results.sort(key=lambda x: x[0], reverse=True)
-    
-    if not results:
-        return "No sections found matching the query in the local cache."
-        
-    out = f"## 📚 Local Cache Results for '{query}'\n\n"
-    for r in results[:limit]:
-        sec = r[1]
-        out += f"**{sec.get('act_name')} — Section {sec.get('number')}**\n"
-        out += f"*{sec.get('title')}*\n"
-        out += f"{sec.get('text')}\n\n---\n\n"
-        
-    return out
-
-@mcp.tool()
-async def vector_search_lex_api(clinical_scenario: str) -> str:
-    """
-    Translates a plain-English clinical scenario (e.g. 'Patient wants to leave but lacks capacity') 
-    into relevant UK legislation by querying the i.AI Lex API semantic vector search engine.
-    This searches across the entire legislation database, not just the local cache.
-    """
-    url = f'{BASE_URL}/legislation/section/search'
-    payload = {
-        'query': clinical_scenario,
-        'limit': 5
-    }
-    
-    try:
-        async with httpx.AsyncClient() as client:
-            r = await client.post(url, json=payload, timeout=15.0)
-            if r.status_code != 200:
-                return f"Lex API Vector Search Failed: Status Code {r.status_code}"
-                
-            data = r.json()
-            if not isinstance(data, list) or not data:
-                return "No semantic matches found for this scenario."
-                
-            out = f"## ⚖️ Vector Matches for Scenario:\n*{clinical_scenario}*\n\n"
-            for i, n in enumerate(data, 1):
-                leg_id = n.get("legislation_id", "")
-                
-                # 1. Use the act_name from the API response if available
-                act_name = n.get("act_name", "")
-                
-                # 2. If not, try our known mapping
-                if not act_name:
-                    for known_id, known_name in REVERSE_ACTS.items():
-                        if known_id in leg_id:
-                            act_name = known_name
-                            break
-                
-                # 3. Final fallback: extract from the legislation_id URL
-                if not act_name:
-                    act_name = leg_id.split("/id/")[-1] if "/id/" in leg_id else leg_id or "Legislation"
-                        
-                sec_num = n.get("number", "??")
-                title = n.get("title", "Untitled Section")
-                text = n.get("text", "")
-                
-                out += f"### {i}. {act_name} — Section {sec_num}: {title}\n"
-                out += f"{text[:600]}...\n\n"
-                out += f"Source URI: {n.get('uri', f'https://www.legislation.gov.uk/id/{leg_id}/section/{sec_num}')}\n\n"
-                
-            return out
-    except Exception as e:
-        return f"Error querying Lex Vector API: {str(e)}"
-
-@mcp.tool()
-async def get_official_explanatory_note(act_name: str, section_number: str) -> str:
-    """
-    Dynamically fetches the Official Government Explanatory Note for a specific Act and section.
-    Explanatory Notes are plain English explainers written by the government.
-    Note: Acts passed prior to 1999 (e.g., Mental Health Act 1983) generally do not have these.
-    
-    Args:
-        act_name: The full name of the Act (e.g., 'Mental Capacity Act 2005').
-        section_number: The specific section number as a string (e.g., '3').
-    """
-    url = f'{BASE_URL}/explanatory_note/section/search'
-    payload = {
-        'query': f'"{act_name}" Section {section_number}',
-        'limit': 3
-    }
-    
-    try:
-        async with httpx.AsyncClient() as client:
-            r = await client.post(url, json=payload, timeout=10.0)
-            if r.status_code == 200:
-                data = r.json()
-                if isinstance(data, list):
-                    parent_id = NURSING_ACTS.get(act_name, "")
-                    for note in data:
-                        # Match the parent ID to ensure this note belongs to the right Act
-                        if parent_id and parent_id in note.get('legislation_id', ''):
-                            text = note.get('text', '')
-                            if text:
-                                return f"### Official Explanatory Note ({act_name} S.{section_number})\n\n{text}"
-                                
-        return f"No Official Explanatory Note could be found for '{act_name}' Section {section_number}. The Act may pre-date the 1999 introduction of Explanatory Notes."
-    except Exception as e:
-        return f"Error fetching Explanatory Note: {str(e)}"
-
-if __name__ == "__main__":
-    # Ensure this runs correctly when started via cursor/claude
-    mcp.run()
->>>>>>> a4e257b16d56f80612b7c9ac6d2e7c198fef5bb6

nursing_sections.json

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 [
   {
     "created_at": "2025-10-17T20:10:21.475763Z",