[Code attached] MCP Revolution: The technological foundation for the Internet of Everything in the AI ​​era - Latest research report in March 2025

#  1. Install uv package management tool (recommended to replace pip)#  pip install uv
# 2. Initialize the MCP server project#  uv init server
# 3. Add MCP dependency#  uv add  'mcp[cli]'

# server.py - Basic MCP server implementationfrom  mcp.server.fastmcp  import  FastMCPfrom  typing  import  List ,  Dict ,  Any ,  Optionalimport  datetime
# Create an MCP server instancemcp = FastMCP( "AdvancedAssistantTools" )
# Define calendar tools@mcp.tool( "calendar" , description = "Calendar management and query tool" )class  CalendarTool :    @mcp.function(        name = "query_events" ,        description = "Query calendar events for a specified date"    )    def  query_events (        self,        date:  str  = mcp.parameter( description = "Query date, format: YYYY-MM-DD" )    ) ->  List [ Dict [ str ,  Any ]]:        """Query calendar event demonstration function"""        # In actual applications, this will connect to the real calendar API        # This example is for demonstration only        try :            query_date = datetime.datetime.strptime(date,  "%Y-%m-%d" ).date()            today = datetime.date.today()
            # Simulate data            if  (query_date - today).days >  30 :                return  []            elif  query_date.weekday() >=  5 :   # Weekend                return  [                    { "title" :  "Family Dinner" ,  "start" :  "12:00" ,  "end" :  "14:00" ,  "location" :  "home" }                ]            else :   # working day                return  [                    { "title" :  "Team meeting" ,  "start" :  "09:30" ,  "end" :  "10:30" ,  "location" :  "Conference Room A" },                    { "title" :  "Project Review" ,  "start" :  "14:00" ,  "end" :  "16:00" ,  "location" :  "Online" }                ]        except  ValueError:            return  { "error" :  "Date format error, please use YYYY-MM-DD format" }
# Define document processing tools@mcp.tool( "document" , description= "Document processing tool" )class  DocumentTool :    @mcp.function(        name = "summarize" ,        description= "Automatically summarize document content"    )    def  summarize (        self,        text:  str  = mcp.parameter( description = "text content to be summarized" ),        max_length:  int  = mcp.parameter( description= "Maximum length of summary" , default= 200 )    ) ->  Dict [ str ,  Any ]:        """Document summary demonstration function"""        # In actual implementation, the NLP model will be called for summary        # This example is for demonstration only        if  not  text:            return  { "error" :  "The text content is empty" }
        # Simple implementation: return the first N characters        summary = text[: min (max_length,  len (text))]        return  {            "summary" : summary,            "original_length" :  len (text),            "summary_length" :  len (summary)        }
# Define data analysis tools@mcp.tool( "data_analytics" , description = "Data analysis and visualization tools" )class  DataAnalyticsTool :    @mcp.function(        name = "analyze_time_series" ,        description = "Analyze time series data and return statistical results"    )    def  analyze_time_series (        self,        data:  List [ float ] = mcp.parameter( description = "time series data point list" ),        metric:  str  = mcp.parameter( description = "Analysis indicators: mean, median, trend" , default = "mean" )    ) ->  Dict [ str ,  Any ]:        """Time series data analysis demonstration function"""        if  not  data:            return  { "error" :  "Data is empty" }
        result = { "data_points" :  len (data)}
        if  metric ==  "mean" :            result[ "mean" ] =  sum (data) /  len (data)        elif  metric ==  "median" :            sorted_data =  sorted (data)            mid =  len (sorted_data) //  2            result[ "median" ] = sorted_data[mid]  if  len (data) %  2  ==  1  else  (sorted_data[mid- 1 ] + sorted_data[mid]) /  2        elif  metric ==  "trend" :            # Simple linear trend calculation            if  len (data) <  2 :                result[ "trend" ] =  "insufficient_data"            else :                first_half =  sum (data[: len (data)// 2 ]) / ( len (data)// 2 )                second_half =  sum (data[ len (data)// 2 :]) / ( len (data) -  len (data)// 2 )                result[ "trend" ] =  "increasing"  if  second_half > first_half  else  "decreasing"  if  second_half < first_half  else  "stable"
        return  result
# Start the serverif  __name__ ==  "__main__" :    mcp.run(host= "0.0.0.0" , port= 8000 )

# advanced_server.py - contains dynamic tool discovery and permission controlfrom  mcp.server.fastmcp  import  FastMCPfrom  mcp.security  import  Permission, Role, SecurityContextfrom  typing  import  Dict ,  Any ,  Listimport  os
mcp = FastMCP( "EnterpriseTools" )
# Define the permission modeladmin_role = Role( "admin" ,  "Administrator role, has all permissions" )user_role = Role( "user" ,  "Ordinary user role, only has query permission" )
read_permission = Permission( "read" ,  "Read data permission" )write_permission = Permission( "write" ,  "write data permission" )
admin_role.add_permissions([read_permission, write_permission])user_role.add_permissions([read_permission])
# Register security contextsecurity_context = SecurityContext()security_context.add_roles([admin_role, user_role])mcp.set_security_context(security_context)
# Enterprise Database Tools@mcp.tool( "database" , description= "Enterprise database access tool" )class  DatabaseTool :    @mcp.function(        name = "query_data" ,        description = "Query enterprise data" ,        required_permissions=[ "read" ]   # Requires read permission    )    def  query_data (        self,        table:  str  = mcp.parameter( description = "table name" ),        filters:  Dict [ str ,  Any ] = mcp.parameter( description = "query condition" )    ) ->  Dict [ str ,  Any ]:        # The actual implementation will connect to the database        # This example is for demonstration only        return  {            "status" :  "success" ,            "data" : [{ "id" :  1 ,  "name" :  "Sample data" }],            "metadata" : { "table" : table,  "filters" : filters}        }
    @mcp.function(        name = "update_data" ,        description = "Update enterprise data" ,        required_permissions=[ "write" ]   # Requires write permission    )    def  update_data (        self,        table:  str  = mcp.parameter( description = "table name" ),        record_id:  int  = mcp.parameter( description = "record ID" ),        updates:  Dict [ str ,  Any ] = mcp.parameter( description = "update content" )    ) ->  Dict [ str ,  Any ]:        # Permission checking is done automatically at the framework level        return  {            "status" :  "success" ,            "updated_id" : record_id,            "metadata" : { "table" : table,  "updates" : updates}        }
# Dynamic tool registrationclass  ToolRegistry :    def  __init__ ( self, mcp_server ):        self.mcp = mcp_server        self.tool_directory =  "./plugins"
    def  scan_and_register ( self ):        """Scan plugin directory and register tool"""        if  not  os.path.exists(self.tool_directory):            os.makedirs(self.tool_directory)
        for  filename  in  os.listdir(self.tool_directory):            if  filename.endswith( ".py" )  and  not  filename.startswith( "_" ):                module_path = os.path.join(self.tool_directory, filename)                self.register_tool_from_file(module_path)
    def  register_tool_from_file ( self, file_path ):        """Dynamically register tools from files"""        # The actual implementation will dynamically load Python modules        # This is just an example        tool_name = os.path.basename(file_path).replace( ".py" ,  "" )        print ( f"Found tool: {tool_name}  from  {file_path} " )        # Dynamic loading code in actual implementation
# Scan and register tools before starting the serverif  __name__ ==  "__main__" :    registry = ToolRegistry(mcp)    registry.scan_and_register()    mcp.run(host= "0.0.0.0" , port= 8000 )

# knowledge_management.pyfrom  mcp.server.fastmcp  import  FastMCPfrom  typing  import  Dict ,  Any ,  List ,  Optionalimport  datetime
mcp = FastMCP( "EnterpriseKnowledge" )
@mcp.tool( "knowledge_base" , description= "Enterprise knowledge base management tool" )class  KnowledgeBaseTool :    def  __init__ ( self ):        # In actual implementation, it will be connected to the real knowledge base system        # The following is for demonstration only        self.knowledge_base = {            "product_specs" : {                "last_updated" :  "2025-03-15" ,                "categories" : [ "Hardware" ,  "Software" ,  "Services" ],                "total_documents" :  1250            },            "technical_docs" : {                "last_updated" :  "2025-03-10" ,                "categories" : [ "API documentation" ,  "Architecture design" ,  "Operation manual" ],                "total_documents" :  3780            },            "company_policies" : {                "last_updated" :  "2025-02-28" ,                "categories" : [ "Human Resources" ,  "Finance" ,  "IT Security" ],                "total_documents" :  420            }        }
    @mcp.function(        name = "search_documents" ,        description= "Search the enterprise knowledge base for documents"    )    def  search_documents (        self,        query:  str  = mcp.parameter( description = "Search keywords" ),        category:  Optional [ str ] = mcp.parameter( description = "Document Category" , default = None ),        max_results:  int  = mcp.parameter( description= "Maximum number of results" , default= 10 )    ) ->  Dict [ str ,  Any ]:        """Search the enterprise knowledge base"""        # The actual implementation will connect to the search engine        # Example returns simulated data        results = []
        if  "product"  in  query.lower():            results.append({                "title" :  "Product Specifications v2.5" ,                "category" :  "Product Documentation" ,                "last_updated" :  "2025-03-01" ,                "relevance" :  0.95 ,                "summary" :  "Details technical specifications, compatibility information, and usage recommendations for all of the company's products."            })
        if  "security"  in  query.lower()  or  "security"  in  query:            results.append({                "title" :  "Enterprise IT Security Specification 2025 Edition" ,                "category" :  "Company Policy" ,                "last_updated" :  "2025-01-15" ,                "relevance" :  0.88 ,                "summary" :  "Specifies the security usage specifications, access control policies and data protection requirements for enterprise IT systems."            })
        # Filter by category        if  category:            results = [doc  for  doc  in  results  if  category.lower()  in  doc[ "category" ].lower()]
        # Limit the number of results        results = results[: min ( len (results), max_results)]
        return  {            "query" : query,            "total_matches" :  len (results),            "results" : results,            "metadata" : {                "search_time" : datetime.datetime.now().isoformat(),                "index_coverage" :  "full" ,                "applied_filters" : { "category" : category}  if  category  else  {}            }        }
    @mcp.function(        name = "get_knowledge_stats" ,        description= "Get knowledge base statistics"    )    def  get_knowledge_stats ( self ) ->  Dict [ str ,  Any ]:        """Get knowledge base statistics"""        total_docs =  sum (category[ "total_documents" ]  for  category  in  self.knowledge_base.values())        all_categories = []        for  kb_section  in  self.knowledge_base.values():            all_categories.extend(kb_section[ "categories" ])
        return  {            "total_documents" : total_docs,            "categories" :  list ( set (all_categories)),            "last_system_update" :  "2025-03-15T08:30:00" ,            "sections" :  list (self.knowledge_base.keys())        }
# Start the serverif  __name__ ==  "__main__" :    mcp.run(host= "0.0.0.0" , port= 8001 )

#customer_service.pyfrom  mcp.server.fastmcp  import  FastMCPfrom  typing  import  Dict ,  Any ,  List ,  Optionalimport  uuidimport  datetime
mcp = FastMCP( "CustomerServiceSystem" )
@mcp.tool( "customer_support" , description= "Customer service and work order management tool" )class  CustomerSupportTool :    def  __init__ ( self ):        # Simulate the work order database        self.tickets = {}        self.knowledge_articles = [            { "id" :  "KA-001" ,  "title" :  "How to reset your password" ,  "category" :  "Account Management" },            { "id" :  "KA-002" ,  "title" :  "Order Refund Process" ,  "category" :  "Payment and Refund" },            { "id" :  "KA-003" ,  "title" :  "Product Activation Guide" ,  "category" :  "Product Usage" }        ]
    @mcp.function(        name = "create_ticket" ,        description = "Create a customer ticket"    )    def  create_ticket (        self,        customer_email:  str  = mcp.parameter( description = "Customer Email" ),        subject:  str  = mcp.parameter( description = "Ticket Subject" ),        description:  str  = mcp.parameter( description = "Problem description" ) ,        priority:  str  = mcp.parameter( description= "Priority: low, medium, high" , default= "medium" ),        category:  str  = mcp.parameter( description = "Question Category" , default = "general" )    ) ->  Dict [ str ,  Any ]:        """Create a new customer ticket"""        # Generate a work order ID        ticket_id =  f"TK- {uuid.uuid4(). hex [: 8 ]} "
        # Create a work order record        ticket = {            "id" : ticket_id,            "customer_email" : customer_email,            "subject" : subject,            "description" : description,            "priority" : priority,            "category" : category,            "status" :  "open" ,            "created_at" : datetime.datetime.now().isoformat(),            "updated_at" : datetime.datetime.now().isoformat(),            "assigned_to" :  None        }
        # Storage Tickets        self.tickets[ticket_id] = ticket
        return  {            "status" :  "success" ,            "ticket_id" : ticket_id,            "message" :  "Work order created successfully" ,            "ticket" : ticket        }
    @mcp.function(        name = "update_ticket" ,        description = "Update work order status"    )    def  update_ticket (        self,        ticket_id:  str  = mcp.parameter( description = "Ticket ID" ),        status:  Optional [ str ] = mcp.parameter( description = "Work order status" , default = None ),        notes:  Optional [ str ] = mcp.parameter( description = "Update Notes" , default = None ),        assigned_to:  Optional [ str ] = mcp.parameter( description = "assigned to" , default = None )    ) ->  Dict [ str ,  Any ]:        """Update work order status"""        if  ticket_id  not  in  self.tickets:            return  {                "status" :  "error" ,                "message" :  f"Cannot find ticket  {ticket_id} "            }
        ticket = self.tickets[ticket_id]
        # Update work order information        if  status:            ticket[ "status" ] = status        if  notes:            if  "notes"  not  in  ticket:                ticket[ "notes" ] = []            ticket[ "notes" ].append({                "content" : notes,                "timestamp" : datetime.datetime.now().isoformat()            })        if  assigned_to:            ticket[ "assigned_to" ] = assigned_to
        ticket[ "updated_at" ] = datetime.datetime.now().isoformat()
        return  {            "status" :  "success" ,            "message" :  "Ticket updated" ,            "ticket" : ticket        }
    @mcp.function(        name= "search_knowledge_base" ,        description= "Search the Customer Service Knowledge Base"    )    def  search_knowledge_base (        self,        query:  str  = mcp.parameter( description = "Search keywords" ),        category:  Optional [ str ] = mcp.parameter( description = "Article Category" , default = None )    ) ->  Dict [ str ,  Any ]:        """Search the Customer Service Knowledge Base for articles"""        results = []
        # Simple keyword matching        for  article  in  self.knowledge_articles:            if  query.lower()  in  article[ "title" ].lower():                if  category  and  category != article[ "category" ]:                    continue                results.append(article)
        return  {            "query" : query,            "results_count" :  len (results),            "results" : results        }
# Start the serverif  __name__ ==  "__main__" :    mcp.run(host= "0.0.0.0" , port= 8002 )

#multi_agent_system.pyfrom  mcp.server.fastmcp  import  FastMCPfrom  mcp.client  import  McpClientfrom  typing  import  Dict ,  Any ,  Listimport  asyncioimport  json
# Main coordinating agent servercoordinator_mcp = FastMCP( "CoordinatorAgent" )
@coordinator_mcp.tool( "task_management" , description= "Task decomposition and coordination tool" )class  TaskManagementTool :    @coordinator_mcp.function(        name = "decompose_task" ,        description = "Break down complex tasks into subtasks"    )    def  decompose_task (        self,        task_description:  str  = coordinator_mcp.parameter( description = "Task Description" ) ,        complexity:  str  = coordinator_mcp.parameter( description = "Task complexity: low, medium, high" , default = "medium" )    ) ->  Dict [ str ,  Any ]:        """Break down complex tasks into subtasks"""        # In actual implementation, a more complex task decomposition algorithm will be used        subtasks = []
        if  "Data Analysis"  in  task_description:            subtasks.extend([                { "id" :  "subtask-1" ,  "type" :  "data_collection" ,  "description" :  "Collect relevant data" },                { "id" :  "subtask-2" ,  "type" :  "data_processing" ,  "description" :  "Data cleaning and preprocessing" },                { "id" :  "subtask-3" ,  "type" :  "data_analysis" ,  "description" :  "Data analysis and insight extraction" },                { "id" :  "subtask-4" ,  "type" :  "report_generation" ,  "description" :  "Generate analysis report" }            ])        elif  "Content Creation"  in  task_description:            subtasks.extend([                { "id" :  "subtask-1" ,  "type" :  "research" ,  "description" :  "Topic research" },                { "id" :  "subtask-2" ,  "type" :  "outline" ,  "description" :  "Create a content outline" },                { "id" :  "subtask-3" ,  "type" :  "draft" ,  "description" :  "Write a first draft" },                { "id" :  "subtask-4" ,  "type" :  "edit" ,  "description" :  "Editing and polishing" }            ])        else :            # Default decomposition scheme            subtasks.extend([                { "id" :  "subtask-1" ,  "type" :  "research" ,  "description" :  "Background research" },                { "id" :  "subtask-2" ,  "type" :  "execution" ,  "description" :  "Execute the main task" },                { "id" :  "subtask-3" ,  "type" :  "review" ,  "description" :  "Results review" }            ])
        return  {            "original_task" : task_description,            "subtasks" : subtasks,            "total_subtasks" :  len (subtasks),            "estimated_complexity" : complexity        }
    @coordinator_mcp.function(        name = "assign_subtasks" ,        description = "Assign subtasks to specialized agents"    )    def  assign_subtasks (        self,        subtasks:  List [ Dict [ str ,  Any ]] = coordinator_mcp.parameter( description = "Subtask list" ),        available_agents:  List [ str ] = coordinator_mcp.parameter( description = "Available agent list" )    ) ->  Dict [ str ,  Any ]:        """Assign subtasks to specialized agents"""        assignments = {}
        # Simple task allocation logic        for  subtask  in  subtasks:            subtask_type = subtask[ "type" ]
            # Choose the appropriate agent based on the task type            if  subtask_type ==  "data_collection"  or  subtask_type ==  "data_processing" :                if  "data_agent"  in  available_agents:                    agent =  "data_agent"                else :                    agent = available_agents[ 0 ]   # Use the first available agent by default            elif  subtask_type ==  "research" :                if  "research_agent"  in  available_agents:                    agent =  "research_agent"                else :                    agent = available_agents[ 0 ]            else :                # Polling to assign other types of tasks                agent = available_agents[ len (assignments) %  len (available_agents)]
            # Record allocation results            if  agent  not  in  assignments:                assignments[agent] = []            assignments[agent].append(subtask[ "id" ])
        return  {            "assignments" : assignments,            "total_assigned" :  sum ( len (tasks)  for  tasks  in  assignments.values())        }
# Professional Agent Server Example - Data Analysis Agentdata_agent_mcp = FastMCP( "DataAnalysisAgent" )
@data_agent_mcp.tool( "data_analysis" , description = "Data Analysis Tool" )class  DataAnalysisTool :    @data_agent_mcp.function(        name = "process_data" ,        description= "Process and analyze data"    )    def  process_data (        self,        data_source:  str  = data_agent_mcp.parameter( description = "data source" ),        analysis_type:  str  = data_agent_mcp.parameter( description= "Analysis type: descriptive, predictive, prescriptive" )    ) ->  Dict [ str ,  Any ]:        """Process and analyze data"""        # Actual data analysis will be performed in the actual implementation        result = {            "status" :  "completed" ,            "data_source" : data_source,            "analysis_type" : analysis_type,            "results" : {                "summary_statistics" : {                    "count" :  1000 ,                    "mean" :  45.7 ,                    "median" :  42.3 ,                    "std_dev" :  15.2                },                "key_findings" : [                    "Discover outliers clustered in specific time periods" ,                    "The data show a clear seasonal trend" ,                    "There is a significant correlation between the two key variables"                ]            }        }
        return  result
# Agent Collaboration Client Exampleclass  AgentCollaborationSystem :    def  __init__ ( self ):        self.coordinator_client = McpClient( "http://localhost:8010" )        self.data_agent_client = McpClient( "http://localhost:8011" )        self.research_agent_client = McpClient( "http://localhost:8012" )
    async  def  execute_complex_task ( self, task_description ):        """The complete process of performing complex tasks"""        print ( f"Receive task:  {task_description} " )
        # 1. Task decomposition        decomposition_result =  await  self.coordinator_client.invoke(            "task_management" ,  "decompose_task" ,            { "task_description" : task_description,  "complexity" :  "high" }        )
        subtasks = decomposition_result[ "subtasks" ]        print ( f"The task has been decomposed into  { len (subtasks)}  subtasks" )
        # 2. Task allocation        available_agents = [ "data_agent" ,  "research_agent" ]        assignment_result =  await  self.coordinator_client.invoke(            "task_management" ,  "assign_subtasks" ,            { "subtasks" : subtasks,  "available_agents" : available_agents}        )
        print ( f"Task assignment results:  {json.dumps(assignment_result[ 'assignments' ], indent= 2 )} " )
        # 3. Execute subtasks in parallel        execution_tasks = []
        for  agent, task_ids  in  assignment_result[ "assignments" ].items():            for  task_id  in  task_ids:                # Find the corresponding subtask details                subtask =  next ((t  for  t  in  subtasks  if  t[ "id" ] == task_id),  None )                if  subtask:                    # Perform different tasks according to different agents                    if  agent ==  "data_agent"  and  subtask[ "type" ]  in  [ "data_collection" ,  "data_processing" ]:                        execution_tasks.append(self.data_agent_client.invoke(                            "data_analysis" ,  "process_data" ,                            { "data_source" :  "database" ,  "analysis_type" :  "descriptive" }                        ))
        # Wait for all subtasks to complete        results =  await  asyncio.gather(*execution_tasks)
        # 4. Integrate the results        final_result = {            "original_task" : task_description,            "subtask_results" : results,            "completion_status" :  "success" ,            "timestamp" : datetime.datetime.now().isoformat()        }
        return  final_result
# Start the multi-agent system exampleif  __name__ ==  "__main__" :    # In real applications, these servers will run in different processes or machines    import  threading
    def  run_coordinator ():        coordinator_mcp.run(host= "0.0.0.0" , port= 8010 )
    def  run_data_agent ():        data_agent_mcp.run(host= "0.0.0.0" , port= 8011 )
    # Start the server    threading.Thread(target=run_coordinator).start()    threading.Thread(target=run_data_agent).start()
    # Wait for the server to start    time.sleep( 2 )
    # Create a collaboration system and execute tasks    collaboration_system = AgentCollaborationSystem()    asyncio.run(collaboration_system.execute_complex_task( "Analyze the sales data of the past three months and generate a report" ))