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"""
MissionControlMCP - Enterprise Automation MCP Server
Main server implementation using MCP SDK
"""
import logging
from typing import Any
import sys
import os
# Setup paths
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
# Import MCP SDK
from mcp.server import Server
from mcp.types import Tool, TextContent
# Import tool functions
from tools.pdf_reader import read_pdf
from tools.text_extractor import extract_text
from tools.web_fetcher import fetch_web_content
from tools.rag_search import search_documents
from tools.data_visualizer import visualize_data
from tools.file_converter import convert_file
from tools.email_intent_classifier import classify_email_intent
from tools.kpi_generator import generate_kpis
# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Create MCP server instance
app = Server("mission-control-mcp")
# Tool definitions
TOOLS = [
Tool(
name="pdf_reader",
description="Extract text and metadata from PDF files. Reads all pages and extracts document information.",
inputSchema={
"type": "object",
"properties": {
"file_path": {
"type": "string",
"description": "Path to the PDF file to read"
}
},
"required": ["file_path"]
}
),
Tool(
name="text_extractor",
description="Process and extract information from text. Supports cleaning, summarization, chunking, and keyword extraction.",
inputSchema={
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "Raw text to process"
},
"operation": {
"type": "string",
"description": "Operation: 'clean', 'summarize', 'chunk', or 'keywords'",
"enum": ["clean", "summarize", "chunk", "keywords"],
"default": "clean"
},
"max_length": {
"type": "integer",
"description": "Maximum length for summary or chunk size",
"default": 500
}
},
"required": ["text"]
}
),
Tool(
name="web_fetcher",
description="Fetch and extract content from web URLs. Returns clean text or HTML content with metadata.",
inputSchema={
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "URL to fetch content from"
},
"extract_text_only": {
"type": "boolean",
"description": "Extract only text content (removes HTML)",
"default": True
}
},
"required": ["url"]
}
),
Tool(
name="rag_search",
description="Semantic search using RAG (Retrieval Augmented Generation). Finds relevant documents using vector embeddings.",
inputSchema={
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Search query"
},
"documents": {
"type": "array",
"items": {"type": "string"},
"description": "List of documents to search in"
},
"top_k": {
"type": "integer",
"description": "Number of top results to return",
"default": 3
}
},
"required": ["query", "documents"]
}
),
Tool(
name="data_visualizer",
description="Create data visualizations and charts. Supports bar, line, pie, and scatter charts from JSON or CSV data.",
inputSchema={
"type": "object",
"properties": {
"data": {
"type": "string",
"description": "JSON or CSV string data"
},
"chart_type": {
"type": "string",
"description": "Chart type",
"enum": ["bar", "line", "pie", "scatter"],
"default": "bar"
},
"x_column": {
"type": "string",
"description": "X-axis column name"
},
"y_column": {
"type": "string",
"description": "Y-axis column name"
},
"title": {
"type": "string",
"description": "Chart title",
"default": "Data Visualization"
}
},
"required": ["data"]
}
),
Tool(
name="file_converter",
description="Convert files between formats. Supports PDF↔TXT, TXT↔CSV conversions.",
inputSchema={
"type": "object",
"properties": {
"input_path": {
"type": "string",
"description": "Path to input file"
},
"output_format": {
"type": "string",
"description": "Desired output format",
"enum": ["txt", "csv", "pdf"]
},
"output_path": {
"type": "string",
"description": "Optional output file path"
}
},
"required": ["input_path", "output_format"]
}
),
Tool(
name="email_intent_classifier",
description="Classify email intent using NLP. Identifies inquiry, complaint, request, feedback, meeting, order, urgent, follow-up, thank you, and application intents.",
inputSchema={
"type": "object",
"properties": {
"email_text": {
"type": "string",
"description": "Email text to classify"
}
},
"required": ["email_text"]
}
),
Tool(
name="kpi_generator",
description="Generate business KPIs and insights from data. Calculates revenue, growth, efficiency, customer, and operational metrics.",
inputSchema={
"type": "object",
"properties": {
"data": {
"type": "string",
"description": "JSON string with business data"
},
"metrics": {
"type": "array",
"items": {
"type": "string",
"enum": ["revenue", "growth", "efficiency", "customer", "operational"]
},
"description": "List of metrics to calculate",
"default": ["revenue", "growth", "efficiency"]
}
},
"required": ["data"]
}
)
]
@app.list_tools()
async def list_tools() -> list[Tool]:
"""List all available tools"""
return TOOLS
@app.call_tool()
async def call_tool(name: str, arguments: Any) -> list[TextContent]:
"""
Handle tool execution requests
Args:
name: Tool name
arguments: Tool arguments
Returns:
List of TextContent responses
"""
try:
logger.info(f"Executing tool: {name}")
result = None
if name == "pdf_reader":
result = read_pdf(arguments["file_path"])
elif name == "text_extractor":
result = extract_text(
text=arguments["text"],
operation=arguments.get("operation", "clean"),
max_length=arguments.get("max_length", 500)
)
elif name == "web_fetcher":
result = fetch_web_content(
url=arguments["url"],
extract_text_only=arguments.get("extract_text_only", True)
)
elif name == "rag_search":
result = search_documents(
query=arguments["query"],
documents=arguments["documents"],
top_k=arguments.get("top_k", 3)
)
elif name == "data_visualizer":
result = visualize_data(
data=arguments["data"],
chart_type=arguments.get("chart_type", "bar"),
x_column=arguments.get("x_column"),
y_column=arguments.get("y_column"),
title=arguments.get("title", "Data Visualization")
)
elif name == "file_converter":
result = convert_file(
input_path=arguments["input_path"],
output_format=arguments["output_format"],
output_path=arguments.get("output_path")
)
elif name == "email_intent_classifier":
result = classify_email_intent(arguments["email_text"])
elif name == "kpi_generator":
result = generate_kpis(
data=arguments["data"],
metrics=arguments.get("metrics", ["revenue", "growth", "efficiency"])
)
else:
raise ValueError(f"Unknown tool: {name}")
# Format result as JSON string
import json
result_text = json.dumps(result, indent=2, default=str)
return [TextContent(type="text", text=result_text)]
except Exception as e:
logger.error(f"Error executing tool {name}: {e}", exc_info=True)
error_msg = f"Error executing {name}: {str(e)}"
return [TextContent(type="text", text=error_msg)]
async def main():
"""Main entry point for the MCP server"""
from mcp.server.stdio import stdio_server
async with stdio_server() as (read_stream, write_stream):
logger.info("MissionControlMCP server starting...")
await app.run(
read_stream,
write_stream,
app.create_initialization_options()
)
if __name__ == "__main__":
import asyncio
asyncio.run(main())
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