MCP100 verdict
Not currently recommended. Official Swift SDK implementing MCP client and server components for Swift applications. Capabilities: Build MCP clients; Build MCP servers; Work with tools, resources, prompts and other protocol capabilities; Use the transports documented by the official Swift SDK
MCP100 Assessment
Editorial verdict
Reviewed September 13, 2026
Task Fit
Mapped
Developer workflows
Trust
Status warning
maintenance_source_health; install_supply_chain; advisory_freshness; adoption_evidence_presence; resource_type_requirements
Maintenance
Recheck needed
The source snapshot needs another refresh before maintenance can be interpreted.
Popularity
Signals unavailable
No independently comparable adoption metric is currently stored.
Evidence scope
No MCP-specific repository metric is stored
No MCP-specific package metric is stored
Stars observation date unavailable · Downloads observation date unavailable
Exact implementation repository metric.
No exact package is documented.
Publisher activity observed September 13, 2026
Stale source snapshot - recheck needed
Source relationship: First-party MCP
Reviewed September 13, 2026
Evidence-backed setup
This profile has type-appropriate, source-backed setup rather than an MCP-server client connector configuration.
Capability
Official Swift SDK implementing MCP client and server components for Swift applications. Capabilities: Build MCP clients; Build MCP servers; Work with tools, resources, prompts and other protocol capabilities; Use the transports documented by the official Swift SDK
Not measured yet
Estimated token footprint of the MCP tool schemas exposed to the agent.
Reviewed access
Review the exact access boundary before connecting this MCP to accounts, credentials, or sensitive data.
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github:modelcontextprotocol is listed as First-party MCP in MCP100’s current evidence.
Current official resources:
Original product, capability, tool, and usage documentation from the MCP publisher. Installation sections appear under Install & Agents.
Official Swift SDK for the Model Context Protocol (MCP).
The Model Context Protocol (MCP) defines a standardized way for applications to communicate with AI and ML models. This Swift SDK implements both client and server components according to the 2025-11-25 (latest) version of the MCP specification.
See the Platform Availability section below for platform-specific requirements.
The client component allows your application to connect to MCP servers.
import MCP
// Initialize the client
let client = Client(name: "MyApp", version: "1.0.0")
// Create a transport and connect
let transport = StdioTransport()
let result = try await client.connect(transport: transport)
// Check server capabilities
if result.capabilities.tools != nil {
// Server supports tools (implicitly including tool calling if the 'tools' capability object is present)
}
[!NOTE] The
Client.connect(transport:)method returns the initialization result. This return value is discardable, so you can ignore it if you don't need to check server capabilities.
For local subprocess communication:
// Create a stdio transport (simplest option)
let transport = StdioTransport()
try await client.connect(transport: transport)
For remote server communication:
// Create a streaming HTTP transport
let transport = HTTPClientTransport(
endpoint: URL(string: "http://localhost:8080")!,
streaming: true // Enable Server-Sent Events for real-time updates
)
try await client.connect(transport: transport)
Tools represent functions that can be called by the client:
// List available tools
let (tools, cursor) = try await client.listTools()
print("Available tools: \(tools.map { $0.name }.joined(separator: ", "))")
// Call a tool with arguments and get the result
let (content, isError) = try await client.callTool(
name: "image-generator",
arguments: [
"prompt": "A serene mountain landscape at sunset",
"style": "photorealistic",
"width": 1024,
"height": 768
]
)
// Handle tool content
for item in content {
switch item {
case .text(let text):
print("Generated text: \(text)")
case .image(let data, let mimeType, let metadata):
if let width = metadata?["width"] as? Int,
let height = metadata?["height"] as? Int {
print("Generated \(width)x\(height) image of type \(mimeType)")
// Save or display the image data
}
case .audio(let data, let mimeType):
print("Received audio data of type \(mimeType)")
case .resource(let resource, _, _):
print("Received embedded resource: \(resource)")
case .resourceLink(let uri, let name, _, _, let mimeType, _):
print("Resource link: \(name) at \(uri), type: \(mimeType ?? "unknown")")
}
}
Resources represent data that can be accessed and potentially subscribed to:
// List available resources
let (resources, nextCursor) = try await client.listResources()
print("Available resources: \(resources.map { $0.uri }.joined(separator: ", "))")
// Read a resource
let contents = try await client.readResource(uri: "resource://example")
print("Resource content: \(contents)")
// Subscribe to resource updates if supported
if result.capabilities.resources?.subscribe == true {
try await client.subscribeToResource(uri: "resource://example")
// Register notification handler
await client.onNotification(ResourceUpdatedNotification.self) { message in
let uri = message.params.uri
print("Resource \(uri) updated with new content")
// Fetch the updated resource content
let updatedContents = try await client.readResource(uri: uri)
print("Updated resource content received")
}
}
Prompts represent templated conversation starters:
// List available prompts
let (prompts, nextCursor) = try await client.listPrompts()
print("Available prompts: \(prompts.map { $0.name }.joined(separator: ", "))")
// Get a prompt with arguments
let (description, messages) = try await client.getPrompt(
name: "customer-service",
arguments: [
"customerName": "Alice",
"orderNumber": "ORD-12345",
"issue": "delivery delay"
]
)
// Use the prompt messages in your application
print("Prompt description: \(description)")
for message in messages {
if case .text(text: let text) = message.content {
print("\(message.role): \(text)")
}
}
Completions allow servers to provide autocompletion suggestions for prompt and resource template arguments as users type:
// Request completions for a prompt argument
let completion = try await client.complete(
promptName: "code_review",
argumentName: "language",
argumentValue: "py"
)
// Display suggestions to the user
for value in completion.values {
print("Suggestion: \(value)")
}
if completion.hasMore == true {
print("More suggestions available (total: \(completion.total ?? 0))")
}
You can also provide context with already-resolved arguments:
// First, user selects a language
let languageCompletion = try await client.complete(
promptName: "code_review",
argumentName: "language",
argumentValue: "py"
)
// User selects "python"
// Then get framework suggestions based on the selected language
let frameworkCompletion = try await client.complete(
promptName: "code_review",
argumentName: "framework",
argumentValue: "fla",
context: ["language": .string("python")]
)
// Returns: ["flask"]
Completions work for resource templates as well:
// Get path completions for a resource URI template
let pathCompletion = try await client.complete(
resourceURI: "file:///{path}",
argumentName: "path",
argumentValue: "/usr/"
)
// Returns: ["/usr/bin", "/usr/lib", "/usr/local"]
Sampling allows servers to request LLM completions through the client, enabling agentic behaviors while maintaining human-in-the-loop control. Clients register a handler to process incoming sampling requests from servers.
[!TIP] Sampling requests flow from server to client, not client to server. This enables servers to request AI assistance while clients maintain control over model access and user approval.
// Register a sampling handler in the client
await client.withSamplingHandler { parameters in
// Review the sampling request (human-in-the-loop step 1)
print("Server requests completion for: \(parameters.messages)")
// Optionally modify the request based on user input
var messages = parameters.messages
if let systemPrompt = parameters.systemPrompt {
print("System prompt: \(systemPrompt)")
}
// Sample from your LLM (this is where you'd call your AI service)
let completion = try await callYourLLMService(
messages: messages,
maxTokens: parameters.maxTokens,
temperature: parameters.temperature
)
// Review the completion (human-in-the-loop step 2)
print("LLM generated: \(completion)")
// User can approve, modify, or reject the completion here
// Return the result to the server
return CreateSamplingMessage.Result(
model: "your-model-name",
stopReason: .endTurn,
role: .assistant,
content: .text(completion)
)
}
Elicitation allows servers to request structured information directly from users through the client. This is useful when servers need user input that wasn't provided in the original request, such as credentials, configuration choices, or approval for sensitive operations.
[!TIP] Elicitation requests flow from server to client, similar to sampling. Clients must register a handler to respond to elicitation requests from servers.
Register an elicitation handler to respond to server requests:
// Register an elicitation handler in the client
await client.withElicitationHandler { parameters in
switch parameters {
case .form(let form):
// Display the request to the user
print("Server requests: \(form.message)")
// If a schema was provided, inspect it
if let schema = form.requestedSchema {
print("Required fields: \(schema.required ?? [])")
print("Schema: \(schema.properties)")
}
// Present UI to collect user input
let userResponse = presentElicitationUI(form)
// Return the user's response
if userResponse.accepted {
return CreateElicitation.Result(
action: .accept,
content: userResponse.data
)
} else if userResponse.canceled {
return CreateElicitation.Result(action: .cancel)
} else {
return CreateElicitation.Result(action: .decline)
}
case .url(let url):
// Direct the user to an external URL (e.g., for OAuth)
openURL(url.url)
return CreateElicitation.Result(action: .accept)
}
}
Common use cases for elicitation:
Roots define the filesystem boundaries that a client exposes to servers. Servers discover roots by sending a roots/list request to the client; clients notify servers when the list changes.
[!TIP] To use roots, declare the
rootscapability when creating the client.
let client = Client(
name: "MyApp",
version: "1.0.0",
capabilities: .init(
roots: .init(listChanged: true)
)
)
// Register a handler for roots/list requests from servers
await client.withRootsHandler {
return [
Root(uri: "file:///Users/user/projects", name: "Projects"),
Root(uri: "file:///Users/user/documents", name: "Documents")
]
}
// Notify connected servers whenever roots change
try await client.notifyRootsChanged()
Clients can control server logging levels and receive structured log messages:
// Set the minimum logging level
try await client.setLoggingLevel(.warning)
// Register a handler for log messages from the server
await client.onNotification(LogMessageNotification.self) { message in
let level = message.params.level // LogLevel (debug, info, warning, etc.)
let logger = message.params.logger // Optional logger name
let data = message.params.data // Arbitrary JSON data
// Display log message based on level
switch level {
case .error, .critical, .alert, .emergency:
print("❌ [\(logger ?? "server")] \(data)")
case .warning:
print("⚠️ [\(logger ?? "server")] \(data)")
default:
print("ℹ️ [\(logger ?? "server")] \(data)")
}
}
Log levels follow the standard syslog severity levels (RFC 5424):
Handle common client errors:
do {
try await client.connect(transport: transport)
// Success
} catch let error as MCPError {
print("MCP Error: \(error.localizedDescription)")
} catch {
print("Unexpected error: \(error)")
}
Either side can cancel an in-progress request and handle incoming cancellations gracefully:
For common operations like tool calls, use the overloaded method that returns RequestContext:
// Call a tool and get a context for cancellation
let context = try client.callTool(
name: "long-running-analysis",
arguments: ["data": largeDataset]
)
// You can cancel the request at any time
try await client.cancelRequest(context.requestID, reason: "User cancelled")
// Await the result (will throw CancellationError if cancelled)
do {
let result = try await context.value
print("Result: \(result.content)")
} catch is CancellationError {
print("Request was cancelled")
}
For full control or custom requests, use send() directly:
// Create any request type
let request = CallTool.request(.init(
name: "long-running-analysis",
arguments: ["data": largeDataset]
))
// Send and get a context for cancellation tracking
let context: RequestContext<CallTool.Result> = try client.send(request)
// Cancel when needed
try await client.cancelRequest(context.requestID, reason: "Timeout")
// Get the result
let result = try await context.value
let content = result.content
let isError = result.isError
Clients can attach a progress token to a request and receive incremental progress updates for long-running operations:
// Call a tool with progress tracking
let progressToken = ProgressToken.unique()
// Register a notification handler to receive progress updates
await client.onNotification(ProgressNotification.self) { message in
let params = message.params
// Filter by your progress token
if params.progressToken == progressToken {
print("Progress: \(params.progress)/\(params.total ?? 0)")
if let message = params.message {
print("Status: \(message)")
}
}
}
// Make the request with the progress token
let (content, isError) = try await client.callTool(
name: "long-running-tool",
arguments: ["input": "value"],
meta: Metadata(progressToken: progressToken)
)
Configure client behavior for capability checking:
// Strict configuration - fail fast if a capability is missing
let strictClient = Client(
name: "StrictClient",
version: "1.0.0",
configuration: .strict
)
// With strict configuration, calling a method for an unsupported capability
// will throw an error immediately without sending a request
do {
// This will throw an error if resources.list capability is not available
let resources = try await strictClient.listResources()
} catch let error as MCPError {
print("Capability not available: \(error.localizedDescription)")
}
// Default (non-strict) configuration - attempt the request anyway
let client = Client(
name: "FlexibleClient",
version: "1.0.0",
configuration: .default
)
// With default configuration, the client will attempt the request
// even if the capability wasn't advertised by the server
do {
let resources = try await client.listResources()
} catch let error as MCPError {
// Still handle the error if the server rejects the request
print("Server rejected request: \(error.localizedDescription)")
}
Improve performance by sending multiple requests in a single batch:
// Array to hold tool call tasks
var toolTasks: [Task<CallTool.Result, Swift.Error>] = []
// Send a batch of requests
try await client.withBatch { batch in
// Add multiple tool calls to the batch
for i in 0..<10 {
toolTasks.append(
try await batch.addRequest(
CallTool.request(.init(name: "square", arguments: ["n": Value(i)]))
)
)
}
}
// Process results after the batch is sent
print("Processing \(toolTasks.count) tool results...")
for (index, task) in toolTasks.enumerated() {
do {
let result = try await task.value
print("\(index): \(result.content)")
} catch {
print("\(index) failed: \(error)")
}
}
You can also batch different types of requests:
// Declare task variables
var pingTask: Task<Ping.Result, Error>?
var promptTask: Task<GetPrompt.Result, Error>?
// Send a batch with different request types
try await client.withBatch { batch in
pingTask = try await batch.addRequest(Ping.request())
promptTask = try await batch.addRequest(
GetPrompt.request(.init(name: "greeting"))
)
}
// Process individual results
do {
if let pingTask = pingTask {
try await pingTask.value
print("Ping successful")
}
if let promptTask = promptTask {
let promptResult = try await promptTask.value
print("Prompt: \(promptResult.description ?? "None")")
}
} catch {
print("Error processing batch results: \(error)")
}
[!NOTE]
Serverautomatically handles batch requests from MCP clients.
The server component allows your application to host model capabilities and respond to client requests.
import MCP
// Create a server with given capabilities
let server = Server(
name: "MyModelServer",
version: "1.0.0",
capabilities: .init(
completions: .init(),
logging: .init(),
prompts: .init(listChanged: true),
resources: .init(subscribe: true, listChanged: true),
tools: .init(listChanged: true)
)
)
// Create transport and start server
let transport = StdioTransport()
try await server.start(transport: transport)
// Now register handlers for the capabilities you've enabled
Register tool handlers to respond to client tool calls:
// Register a tool list handler
await server.withMethodHandler(ListTools.self) { _ in
let tools = [
Tool(
name: "weather",
description: "Get current weather for a location",
inputSchema: .object([
"properties": .object([
"location": .string("City name or coordinates"),
"units": .string("Units of measurement, e.g., metric, imperial")
])
])
),
Tool(
name: "calculator",
description: "Perform calculations",
inputSchema: .object([
"properties": .object([
"expression": .string("Mathematical expression to evaluate")
])
])
)
]
return .init(tools: tools)
}
// Register a tool call handler
await server.withMethodHandler(CallTool.self) { params in
switch params.name {
case "weather":
let location = params.arguments?["location"]?.stringValue ?? "Unknown"
let units = params.arguments?["units"]?.stringValue ?? "metric"
let weatherData = getWeatherData(location: location, units: units) // Your implementation
return .init(
content: [.text("Weather for \(location): \(weatherData.temperature)°, \(weatherData.conditions)")],
isError: false
)
case "calculator":
if let expression = params.arguments?["expression"]?.stringValue {
let result = evaluateExpression(expression) // Your implementation
return .init(content: [.text("\(result)")], isError: false)
} else {
return .init(content: [.text("Missing expression parameter")], isError: true)
}
default:
return .init(content: [.text("Unknown tool")], isError: true)
}
}
Implement resource handlers for data access:
// Register a resource list handler
await server.withMethodHandler(ListResources.self) { params in
let resources = [
Resource(
name: "Knowledge Base Articles",
uri: "resource://knowledge-base/articles",
description: "Collection of support articles and documentation"
),
Resource(
name: "System Status",
uri: "resource://system/status",
description: "Current system operational status"
)
]
return .init(resources: resources, nextCursor: nil)
}
// Register a resource read handler
await server.withMethodHandler(ReadResource.self) { params in
switch params.uri {
case "resource://knowledge-base/articles":
return .init(contents: [Resource.Content.text("# Knowledge Base\n\nThis is the content of the knowledge base...", uri: params.uri)])
case "resource://system/status":
let status = getCurrentSystemStatus() // Your implementation
let statusJson = """
{
"status": "\(status.overall)",
"components": {
"database": "\(status.database)",
"api": "\(status.api)",
"model": "\(status.model)"
},
"lastUpdated": "\(status.timestamp)"
}
"""
return .init(contents: [Resource.Content.text(statusJson, uri: params.uri, mimeType: "application/json")])
default:
throw MCPError.invalidParams("Unknown resource URI: \(params.uri)")
}
}
// Register a resource subscribe handler
await server.withMethodHandler(ResourceSubscribe.self) { params in
// Store subscription for later notifications.
// Client identity for multi-client scenarios needs to be managed by the server application,
// potentially using information from the initialize handshake if the server handles one client post-init.
// addSubscription(clientID: /* some_client_identifier */, uri: params.uri)
print("Client subscribed to \(params.uri). Server needs to implement logic to track this subscription.")
return .init()
}
Implement prompt handlers:
// Register a prompt list handler
await server.withMethodHandler(ListPrompts.self) { params in
let prompts = [
Prompt(
name: "interview",
description: "Job interview conversation starter",
arguments: [
.init(name: "position", description: "Job position", required: true),
.init(name: "company", description: "Company name", required: true),
.init(name: "interviewee", description: "Candidate name")
]
),
Prompt(
name: "customer-support",
description: "Customer support conversation starter",
arguments: [
.init(name: "issue", description: "Customer issue", required: true),
.init(name: "product", description: "Product name", required: true)
]
)
]
return .init(prompts: prompts, nextCursor: nil)
}
// Register a prompt get handler
await server.withMethodHandler(GetPrompt.self) { params in
switch params.name {
case "interview":
let position = params.arguments?["position"]?.stringValue ?? "Software Engineer"
let company = params.arguments?["company"]?.stringValue ?? "Acme Corp"
let interviewee = params.arguments?["interviewee"]?.stringValue ?? "Candidate"
let description = "Job interview for \(position) position at \(company)"
let messages: [Prompt.Message] = [
.user(.text(text: "You are an interviewer for the \(position) position at \(company).")),
.user(.text(text: "Hello, I'm \(interviewee) and I'm here for the \(position) interview.")),
.assistant(.text(text: "Hi \(interviewee), welcome to \(company)! I'd like to start by asking about your background and experience."))
]
return .init(description: description, messages: messages)
case "customer-support":
// Similar implementation for customer support prompt
default:
throw MCPError.invalidParams("Unknown prompt name: \(params.name)")
}
}
Servers can provide autocompletion suggestions for prompt and resource template arguments:
// Enable completions capability
let server = Server(
name: "MyServer",
version: "1.0.0",
capabilities: .init(
completions: .init(),
prompts: .init(listChanged: true)
)
)
// Register a completion handler
await server.withMethodHandler(Complete.self) { params in
// Get the argument being completed
let argumentName = params.argument.name
let currentValue = params.argument.value
// Check which prompt or resource is being completed
switch params.ref {
case .prompt(let promptRef):
// Provide completions for a prompt argument
if promptRef.name == "code_review" && argumentName == "language" {
// Simple prefix matching
let allLanguages = ["python", "perl", "php", "javascript", "java", "swift"]
let matches = allLanguages.filter { $0.hasPrefix(currentValue.lowercased()) }
return .init(
completion: .init(
values: Array(matches.prefix(100)), // Max 100 items
total: matches.count,
hasMore: matches.count > 100
)
)
}
case .resource(let resourceRef):
// Provide completions for a resource template argument
if resourceRef.uri == "file:///{path}" && argumentName == "path" {
// Return directory suggestions
let suggestions = try getDirectoryCompletions(for: currentValue)
return .init(
completion: .init(
values: suggestions,
total: suggestions.count,
hasMore: false
)
)
}
}
// No completions available
return .init(completion: .init(values: [], total: 0, hasMore: false))
}
You can also use context from already-resolved arguments:
await server.withMethodHandler(Complete.self) { params in
// Access context from previous argument completions
if let context = params.context,
let language = context.arguments["language"]?.stringValue {
// Provide framework suggestions based on selected language
if language == "python" {
let frameworks = ["flask", "django", "fastapi", "tornado"]
let matches = frameworks.filter {
$0.hasPrefix(params.argument.value.lowercased())
}
return .init(
completion: .init(values: matches, total: matches.count, hasMore: false)
)
}
}
return .init(completion: .init(values: [], total: 0, hasMore: false))
}
Servers can request LLM completions from clients through sampling. This enables agentic behaviors where servers can ask for AI assistance while maintaining human oversight.
// Enable sampling capability in server
let server = Server(
name: "MyModelServer",
version: "1.0.0",
capabilities: .init(
sampling: .init(), // Enable sampling capability
tools: .init(listChanged: true)
)
)
// Request sampling from the connected client
do {
let result = try await server.requestSampling(
messages: [
.user("Analyze this data and suggest next steps")
],
systemPrompt: "You are a helpful data analyst",
temperature: 0.7,
maxTokens: 150
)
// Use the LLM completion in your server logic
print("LLM suggested: \(result.content)")
} catch {
print("Sampling request failed: \(error)")
}
Sampling enables powerful agentic workflows:
Servers can request information from users through elicitation:
// Ask the user to provide some additional information
let schema = Elicitation.RequestSchema(
title: "Additional Information Required",
description: "Please provide the following details to continue",
properties: [
"name": .object([
"type": .string("string"),
"description": .string("Your full name")
]),
"confirmed": .object([
"type": .string("boolean"),
"description": .string("Do you confirm the provided information?")
])
],
required: ["name", "confirmed"]
)
let result = try await server.requestElicitation(
message: "Some details are needed before proceeding",
requestedSchema: schema
)
switch result.action {
case .accept:
if let content = result.content {
let name = content["name"]?.stringValue
let confirmed = content["confirmed"]?.boolValue
// Use the collected data...
}
case .decline:
throw MCPError.invalidRequest("User declined to provide information")
case .cancel:
throw MCPError.invalidRequest("Operation canceled by user")
}
For URL-based elicitation (e.g., OAuth flows), use the URL overload:
let result = try await server.requestElicitation(
message: "Please sign in to continue",
url: "https://example.com/oauth/authorize?client_id=...",
elicitationId: UUID().uuidString
)
Servers can request the list of filesystem roots that the client has exposed:
// Request roots from the connected client
// (requires the client to declare the roots capability)
let roots = try await server.listRoots()
for root in roots {
print("Root: \(root.name ?? root.uri) at \(root.uri)")
}
// React to root list changes
await server.onNotification(RootsListChangedNotification.self) { _ in
let updatedRoots = try await server.listRoots()
print("Roots updated: \(updatedRoots.map { $0.uri })")
}
Servers can send structured log messages to clients:
// Enable logging capability
let server = Server(
name: "MyServer",
version: "1.0.0",
capabilities: .init(
logging: .init(),
tools: .init(listChanged: true)
)
)
// Send log messages at different severity levels
try await server.log(
level: .info,
logger: "database",
data: Value.object([
"message": .string("Database connected successfully"),
"host": .string("localhost"),
"port": .int(5432)
])
)
try await server.log(
level: .error,
logger: "api",
data: Value.object([
"message": .string("Request failed"),
"statusCode": .int(500),
"error": .string("Internal server error")
])
)
// You can also use codable types directly
struct ErrorLog: Codable {
let message: String
let code: Int
let timestamp: String
}
let errorLog = ErrorLog(
message: "Operation failed",
code: 500,
timestamp: ISO8601DateFormatter().string(from: Date())
)
try await server.log(level: .error, logger: "operations", data: errorLog)
Clients can control which log levels they receive:
// Register a handler for client's logging level preferences
await server.withMethodHandler(SetLoggingLevel.self) { params in
let minimumLevel = params.level
// Store the client's preference and filter log messages accordingly
// (Implementation depends on your server architecture)
storeLogLevel(minimumLevel)
return Empty()
}
Servers can send incremental progress notifications during long-running tool calls by reading the progressToken from the request metadata and sending ProgressNotification messages:
await server.withMethodHandler(CallTool.self) { params in
// Read the progress token from request metadata
guard let token = params._meta?.progressToken else {
// No progress token provided — run without reporting progress
return .init(content: [.text("Done")], isError: false)
}
// Report initial progress
let started = ProgressNotification.message(
.init(progressToken: token, progress: 0, total: 100)
)
try await server.notify(started)
// ... do work ...
// Report intermediate progress
let halfway = ProgressNotification.message(
.init(progressToken: token, progress: 50, total: 100, message: "Halfway there")
)
try await server.notify(halfway)
// ... do more work ...
// Report completion
let done = ProgressNotification.message(
.init(progressToken: token, progress: 100, total: 100, message: "Complete")
)
try await server.notify(done)
return .init(content: [.text("Done")], isError: false)
}
Control client connections with an initialize hook:
// Start the server with an initialize hook
try await server.start(transport: transport) { clientInfo, clientCapabilities in
// Validate client info
guard clientInfo.name != "BlockedClient" else {
throw MCPError.invalidRequest("This client is not allowed")
}
// You can also inspect client capabilities
if clientCapabilities.sampling == nil {
print("Client does not support sampling")
}
// Perform any server-side setup based on client info
print("Client \(clientInfo.name) v\(clientInfo.version) connected")
// If the hook completes without throwing, initialization succeeds
}
When a server is connected over StatefulHTTPServerTransport or StatelessHTTPServerTransport,
method handlers can observe the originating HTTP request (headers, body, path, method) via
Server.currentHandlerContext — a task-local set automatically before each handler runs:
await server.withMethodHandler(CallTool.self) { params in
let httpRequest = Server.currentHandlerContext?.httpContext
let authHeader = httpRequest?.headers["Authorization"]
// …use the header to scope the response, audit, etc.
return .init(content: [.text("ok")], isError: false)
}
httpContext is nil for transports that don't carry HTTP context (e.g. StdioTransport,
InMemoryTransport) and for handlers reached off the dispatch path.
Task-locals are not inherited by Task.detached. If you spawn a detached task from a handler,
capture the context up front:
let ctx = Server.currentHandlerContext
Task.detached { await doWork(with: ctx?.httpContext) }
Custom HTTP transports can opt in by conforming to HTTPContextProviding and returning the
HTTPRequest for a given JSON-RPC id while it is in flight.
We recommend using Swift Service Lifecycle for managing startup and shutdown of services.
First, add the dependency to your Package.swift:
.package(url: "https://github.com/swift-server/swift-service-lifecycle.git", from: "2.3.0"),
Then implement the MCP server as a Service:
import MCP
import ServiceLifecycle
import Logging
struct MCPService: Service {
let server: Server
let transport: Transport
init(server: Server, transport: Transport) {
self.server = server
self.transport = transport
}
func run() async throws {
// Start the server
try await server.start(transport: transport)
// Keep running until external cancellation
try await Task.sleep(for: .days(365 * 100)) // Effectively forever
}
func shutdown() async throws {
// Gracefully shutdown the server
await server.stop()
}
}
Then use it in your application:
import MCP
import ServiceLifecycle
import Logging
let logger = Logger(label: "com.example.mcp-server")
// Create the MCP server
let server = Server(
name: "MyModelServer",
version: "1.0.0",
capabilities: .init(
prompts: .init(listChanged: true),
resources: .init(subscribe: true, listChanged: true),
tools: .init(listChanged: true)
)
)
// Add handlers directly to the server
await server.withMethodHandler(ListTools.self) { _ in
// Your implementation
return .init(tools: [
Tool(name: "example", description: "An example tool")
])
}
await server.withMethodHandler(CallTool.self) { params in
// Your implementation
return .init(content: [.text("Tool result")], isError: false)
}
// Create MCP service and other services
let transport = StdioTransport(logger: logger)
let mcpService = MCPService(server: server, transport: transport)
let databaseService = DatabaseService() // Your other services
// Create service group with signal handling
let serviceGroup = ServiceGroup(
services: [mcpService, databaseService],
configuration: .init(
gracefulShutdownSignals: [.sigterm, .sigint]
),
logger: logger
)
// Run the service group - this blocks until shutdown
try await serviceGroup.run()
This approach has several benefits:
[ServiceLifecycle](https://swiftpackageindex.com/swift-server/swift-service-lifecycle/documentation/servicelifecycle)
also supports service dependencies, conditional services,
and other useful features.MCP's transport layer handles communication between clients and servers. The Swift SDK provides multiple built-in transports:
| Transport | Description | Platforms | Best for |
|---|---|---|---|
StdioTransport | Implements stdio transport using standard input/output streams | Apple platforms, Linux with glibc | Local subprocesses, CLI tools |
HTTPClientTransport | Implements Streamable HTTP transport using Foundation's URL Loading System | All platforms with Foundation | Remote servers, web applications |
StatelessHTTPServerTransport | HTTP server transport with simple request-response semantics; no session management or SSE streaming |
You can implement a custom transport by conforming to the Transport protocol:
import MCP
import Foundation
public actor MyCustomTransport: Transport {
public nonisolated let logger: Logger
private var isConnected = false
private let messageStream: AsyncThrowingStream<Data, any Swift.Error>
private let messageContinuation: AsyncThrowingStream<Data, any Swift.Error>.Continuation
public init(logger: Logger? = nil) {
self.logger = logger ?? Logger(label: "my.custom.transport")
var continuation: AsyncThrowingStream<Data, any Swift.Error>.Continuation!
self.messageStream = AsyncThrowingStream { continuation = $0 }
self.messageContinuation = continuation
}
public func connect() async throws {
// Implement your connection logic
isConnected = true
}
public func disconnect() async {
// Implement your disconnection logic
isConnected = false
messageContinuation.finish()
}
public func send(_ data: Data) async throws {
// Implement your message sending logic
}
public func receive() -> AsyncThrowingStream<Data, any Swift.Error> {
return messageStream
}
}
HTTPClientTransport supports OAuth 2.1 Bearer token authorization per the
MCP authorization specification.
When a server returns 401 Unauthorized or 403 Forbidden, the transport automatically:
/.well-known/oauth-protected-resourceAuthorization is opt-in and disabled by default.
Pass an OAuthAuthorizer to HTTPClientTransport(authorizer:) to enable it.
Machine-to-machine authentication using a pre-shared client secret:
let config = OAuthConfiguration(
grantType: .clientCredentials,
authentication: .clientSecretBasic(clientID: "my-app", clientSecret: "s3cr3t")
)
let authorizer = OAuthAuthorizer(configuration: config)
let transport = HTTPClientTransport(
endpoint: URL(string: "https://api.example.com/mcp")!,
authorizer: authorizer
)
let client = Client(name: "MyClient", version: "1.0.0")
try await client.connect(transport: transport)
Interactive, browser-based authentication with PKCE.
Implement OAuthAuthorizationDelegate to open the authorization URL and capture the redirect:
struct MyAuthDelegate: OAuthAuthorizationDelegate {
func presentAuthorizationURL(_ url: URL) async throws -> URL {
// Open the URL in a browser/webview and wait for the callback redirect URI.
// The returned URL must include the authorization code and state parameters.
return try await openBrowserAndWaitForCallback(url)
}
}
let config = OAuthConfiguration(
grantType: .authorizationCode,
authentication: .none(clientID: "my-app"),
authorizationDelegate: MyAuthDelegate()
)
let authorizer = OAuthAuthorizer(configuration: config)
let transport = HTTPClientTransport(
endpoint: URL(string: "https://api.example.com/mcp")!,
authorizer: authorizer
)
Supply an externally acquired token (e.g., from a system credential store) via accessTokenProvider.
The SDK calls this closure after discovery completes. Return nil to fall back to the configured grant flow:
let config = OAuthConfiguration(
grantType: .clientCredentials,
authentication: .none(clientID: "my-app"),
accessTokenProvider: { context, session in
// context contains the discovered resource URI, token endpoint, scopes, etc.
return try await KeychainTokenStore.shared.loadToken(for: context.resource)
}
)
By default, tokens are stored in memory and lost when the process exits.
To persist tokens across sessions, implement TokenStorage and pass it to OAuthAuthorizer:
final class KeychainTokenStorage: TokenStorage {
func save(_ token: OAuthAccessToken) {
// Encode and store token.value in the system Keychain
}
func load() -> OAuthAccessToken? {
// Load and decode token from the Keychain
return nil
}
func clear() {
// Delete from the Keychain
}
}
let authorizer = OAuthAuthorizer(
configuration: config,
tokenStorage: KeychainTokenStorage()
)
private_key_jwt AuthenticationAuthenticate to the token endpoint using an asymmetric key (RFC 7523). The SDK provides a built-in ES256 helper for P-256 keys:
let config = OAuthConfiguration(
grantType: .clientCredentials,
authentication: .privateKeyJWT(
clientID: "my-app",
assertionFactory: { tokenEndpoint, clientID in
try OAuthConfiguration.makePrivateKeyJWTAssertion(
clientID: clientID,
tokenEndpoint: tokenEndpoint,
privateKeyPEM: myEC256PrivateKeyPEM // PEM-encoded P-256 private key
)
}
)
)
Skip automatic discovery by providing explicit endpoint URLs. Useful when the server does not publish well-known metadata documents:
let config = OAuthConfiguration(
grantType: .clientCredentials,
authentication: .clientSecretBasic(clientID: "app", clientSecret: "secret"),
endpointOverrides: OAuthConfiguration.EndpointOverrides(
tokenEndpoint: URL(string: "https://auth.example.com/oauth/token")!
)
)
Per the MCP authorization specification, servers MUST serve Protected Resource Metadata
at /.well-known/oauth-protected-resource so clients can discover authorization server endpoints.
Use ProtectedResourceMetadataValidator as the first validator in your pipeline so that
unauthenticated discovery requests are handled before the bearer token check:
let metadata = OAuthProtectedResourceServerMetadata(
resource: "https://api.example.com",
authorizationServers: [URL(string: "https://auth.example.com")!],
scopesSupported: ["read", "write"]
)
let metadataValidator = ProtectedResourceMetadataValidator(metadata: metadata)
Use BearerTokenValidator to authenticate incoming requests.
Your tokenValidator closure MUST verify the token's aud claim to prevent
token substitution attacks where a token intended for another resource is replayed against your server:
let resourceIdentifier = URL(string: "https://api.example.com")!
let bearerValidator = BearerTokenValidator(
resourceMetadataURL: URL(string: "https://api.example.com/.well-known/oauth-protected-resource")!,
resourceIdentifier: resourceIdentifier,
tokenValidator: { token, request, context in
guard let claims = try? verifyAndDecodeJWT(token) else {
return .invalidToken(errorDescription: "Token verification failed")
}
// Pass audience and expiry to BearerTokenInfo; the SDK validates the
// audience claim against resourceIdentifier automatically.
return .valid(BearerTokenInfo(
audience: claims.audience,
expiresAt: claims.expiresAt
))
}
)
let pipeline = StandardValidationPipeline(validators: [
metadataValidator, // serves /.well-known/oauth-protected-resource unauthenticated
bearerValidator, // validates Bearer tokens on all other requests
AcceptHeaderValidator(mode: .sseRequired),
ContentTypeValidator(),
SessionValidator(),
])
The Swift SDK has the following platform requirements:
| Platform | Minimum Version |
|---|---|
| macOS | 13.0+ |
| iOS / Mac Catalyst | 16.0+ |
| watchOS | 9.0+ |
| tvOS | 16.0+ |
| visionOS | 1.0+ |
| Linux | Distributions with glibc or musl, including Ubuntu, Debian, Fedora, and Alpine Linux |
While the core library works on any platform supporting Swift 6 (including Linux), running a client or server requires a compatible transport.
Enable logging to help troubleshoot issues:
import Logging
import MCP
// Configure Logger
LoggingSystem.bootstrap { label in
var handler = StreamLogHandler.standardOutput(label: label)
handler.logLevel = .debug
return handler
}
// Create logger
let logger = Logger(label: "com.example.mcp")
// Pass to client/server
let client = Client(name: "MyApp", version: "1.0.0")
// Pass to transport
let transport = StdioTransport(logger: logger)
This project follows Semantic Versioning. For pre-1.0 releases, minor version increments (0.X.0) may contain breaking changes.
For details about changes in each release, see the GitHub Releases page.
This project is licensed under Apache 2.0 for new contributions, with existing code under MIT. See the LICENSE file for details.
| All platforms with Foundation |
| Simple HTTP servers, serverless/edge functions |
StatefulHTTPServerTransport | HTTP server transport with full session management and SSE streaming for server-initiated messages | All platforms with Foundation | Full-featured HTTP servers, streaming notifications |
InMemoryTransport | Custom in-memory transport for direct communication within the same process | All platforms | Testing, debugging, same-process client-server communication |
NetworkTransport | Custom transport using Apple's Network framework for TCP/UDP connections | Apple platforms only | Low-level networking, custom protocols |