Model-Context-Protocol Servers

A collection of specialized MCP (Model Context Protocol) servers for different use cases.

1. Leafly Cannabis Strain Data Scraper

This MCP server implements a specialized scraper for collecting structured cannabis strain data from Leafly.com, following a standardized schema and methodology.

Installation

Ensure you have Node.js 18+ installed
Clone the repository
Install dependencies:
```
cd firecrawl-mcp-server
npm install
```

Set up environment variables:

# Copy the example .env file
cp .env.example .env

# Edit the .env file and add your Firecrawl API key
# You can obtain an API key from https://mendable.ai/firecrawl
nano .env  # or use any text editor

Build the project:
```
npm run build
```

API Key Requirement

Important: This scraper requires a valid Firecrawl API key to function. If you try to run the scraper without a valid API key, you will receive a 401 Unauthorized error.

You can set the API key in one of two ways:

Environment Variable:

export FIRECRAWL_API_KEY=your_api_key_here

In a .env file:
```
FIRECRAWL_API_KEY=your_api_key_here
```

Features

Scrapes 66 standardized data points for each cannabis strain from Leafly.com
Follows a consistent methodology for data extraction and normalization
Handles cases where data is missing or inconsistent
Exports data in CSV or JSON format
Built-in fallback mechanisms for strains that aren't directly accessible

Implementation Approaches

Regex-Based Extraction

The repository includes a regex-based scraper that extracts data using pattern matching:

// Extract cannabinoids using regex
function extractCannabinoids(content: string, strainData: StrainData): void {
  // THC extraction
  const thcMatch = content.match(/THC\s+(\d+(?:\.\d+)?)-?(\d+(?:\.\d+)?)?\s*%/i);
  if (thcMatch) {
    // Take higher end of range per methodology
    const thcValue = thcMatch[2] ? parseFloat(thcMatch[2]) : parseFloat(thcMatch[1]);
    strainData["cannabinoids.THC"] = thcValue / 100;
  }
  
  // Similar patterns for other cannabinoids
}

LLM-Powered Extraction

The repository also includes an advanced LLM-powered extraction method that uses structured schemas and AI to extract information more accurately:

// Using the extract tool for LLM-powered extraction
const strainSchema = {
  type: 'object',
  properties: {
    "strain_name": { type: 'string' },
    "aliases": { type: 'string' },
    "strain_classification": { type: 'string' },
    "thc_percentage": { type: 'number' },
    "cbd_percentage": { type: 'number' },
    "cbg_percentage": { type: 'number' },
    "terpenes": { 
      type: 'object',
      properties: {
        "myrcene": { type: 'string' },
        "caryophyllene": { type: 'string' }
        // Other terpenes...
      }
    },
    // Other properties...
  }
};

// Extract data using LLM
const extractedData = await client.extract([strainUrl], {
  schema: strainSchema,
  systemPrompt: "Extract precise cannabis strain data. Use exact numbers when available.",
  prompt: `Extract all available data for the cannabis strain "${strain}" according to the schema.`
});

Benefits of LLM extraction:

Better handling of unstructured text and variations in formatting
More resilient to website changes
Can infer missing values based on context
Extracts relationships between data points

Data Structure

The scraper collects the following categories of data for each strain:

Basic Information: Strain name
Terpenes: myrcene, pinene, caryophyllene, limonene, linalool, terpinolene, ocimene, humulene, other
Cannabinoids: THC, CBD, CBG, CBN, other
Medical Effects: Stress, Anxiety, Depression, Pain, Insomnia, Lack of Appetite, Nausea, other
User Effects: Happy, Euphoric, Creative, Relaxed, Uplifted, Energetic, Focused, Sleepy, Hungry, Talkative, Tingly, Giggly, DryMouth, DryEyes, Dizzy, Paranoid, Anxious, other
Onset and Duration: onset_minutes, duration_hours
Interactions: Sedatives, Anti-anxiety (benzodiazepines), Antidepressants (SSRIs), Opioid analgesics, Anticonvulsants, Anticoagulants, other
Flavors: Berry, Sweet, Earthy, Pungent, Pine, Vanilla, Minty, Skunky, Citrus, Spicy, Herbal, Diesel, Tropical, Fruity, Grape, other

Usage

As a Firecrawl MCP Tool

Once integrated with the Firecrawl MCP server, the tool can be called with the following parameters:

{
  "name": "firecrawl_leafly_strain",
  "arguments": {
    "strains": ["Blue Dream", "OG Kush", "Sour Diesel"],
    "exportFormat": "csv"  // or "json"
  }
}

Using the Extract Tool Directly

For more advanced extraction with the LLM-powered approach:

{
  "name": "firecrawl_extract",
  "arguments": {
    "urls": ["https://www.leafly.com/strains/blue-dream"],
    "schema": {
      "type": "object",
      "properties": {
        "strain_name": { "type": "string" },
        "thc_percentage": { "type": "number" },
        "cbd_percentage": { "type": "number" },
        "effects": { "type": "string" },
        "flavors": { "type": "string" },
        "medical": { "type": "string" },
        "terpenes": { "type": "object" }
      }
    },
    "prompt": "Extract comprehensive cannabis strain data from this Leafly page."
  }
}

Using the CLI Script

You can also use the included CLI script to run the scraper directly:

# Set the Firecrawl API key (required)
export FIRECRAWL_API_KEY=your_api_key_here

# Using npm scripts
npm run scrape-leafly -- output.csv "Blue Dream,OG Kush,Sour Diesel"

# Or running directly
node dist/leafly-scraper-cli.js output.csv "Blue Dream,OG Kush,Sour Diesel"

Methodology

The scraper follows a rigorous methodology for extracting and normalizing data:

Lab Data Priority: Lab-tested cannabinoid and terpene data is prioritized when available
Consistent Normalization: When exact values aren't available, standardized normalization is applied:
- For terpenes: dominant = 0.008, second = 0.005, third = 0.003, others = 0.001
- For effects and flavors: Values are normalized to a 0.0-1.0 scale
Default Values: Standard defaults are applied for commonly missing fields

Troubleshooting

TypeScript Errors

If you encounter TypeScript compilation errors:

Ensure you have all dependencies installed: npm install
Make sure TypeScript is installed: npm install -g typescript
TypeScript module errors can typically be fixed by installing the @types packages:
```
npm install --save-dev @types/node
```

2. Python Codebase MCP Server

This MCP server provides code analysis capabilities and file system access for codebase navigation.

Installation

Ensure you have Python 3.7+ installed
Install dependencies:
```
pip install mcp-python-sdk watchdog
```

Features

File system navigation and file reading
Code search functionality
Project structure analysis
Real-time file change monitoring
Function and component discovery
Dependency analysis

Usage

Start the server:

python mcp_server.py

The server provides tools for code analysis:

search_function: Find function definitions in code files
search_code: Search for text across all code files
get_project_structure: Generate a tree-like structure of the project
analyze_dependencies: Analyze project dependencies
find_components: Discover React/React Native components

Resources

/file/list/{directory}: List files in a directory
/file/read/{filepath}: Read file contents
/file/info/{filepath}: Get file metadata
/file/changes/{directory}: Get recently modified files

3. DeepSeek R1 extended MCP Server

This MCP server provides access to DeepSeek AI models for text generation, summarization, and document processing.

Installation

Ensure you have Node.js 14+ installed

Install dependencies:

npm install @modelcontextprotocol/sdk openai dotenv

Set up environment variables:

# Create a .env file
echo "DEEPSEEK_API_KEY=your_api_key_here" > .env

Features

Text generation using DeepSeek R1 model
Text summarization
Streaming text generation
Multi-model support
Document processing (summarize, extract entities, analyze sentiment)
File operations for saving outputs

Usage

Start the server:

node deepseek_mcp.js

The server provides the following tools:

deepseek_r1: Generate text using DeepSeek R1 model
deepseek_summarize: Summarize text
deepseek_stream: Stream text generation
deepseek_multi: Generate text using different DeepSeek models
deepseek_document: Process documents (summarize, extract entities, analyze sentiment)

Resources

/model/info: Get information about supported models
/server/status: Check server status
/file/save/{filename}: Save content to a file
/file/list: List saved files
/file/read/{filename}: Read saved file contents

4. JSON Manager MCP Server

This MCP server provides advanced JSON querying and manipulation capabilities.

Installation

Ensure you have Node.js 14+ installed

Install dependencies:

npm install @modelcontextprotocol/sdk node-fetch jsonpath

Features

Query JSON data using JSONPath
Advanced filtering
String operations
Numeric operations
Date operations
Array transformations
Complex data comparisons
Result caching
Save and manage query results

Usage

Start the server:

node json_mcp.js

The server provides the following tools:

query: Query JSON data using JSONPath expressions
filter: Filter JSON data based on conditions
save_query: Save query results to a file
compare_json: Compare two JSON datasets

Resources

/saved_queries/list: List saved queries
/saved_queries/get/{filename}: Retrieve a saved query
/cache/status: Check cache status
/cache/clear: Clear the cache

License

MIT License