{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-modelcontextprotocol-python-sdk","slug":"mcp-modelcontextprotocol-python-sdk","name":"python-sdk","type":"framework","url":"https://github.com/modelcontextprotocol/python-sdk","page_url":"https://unfragile.ai/mcp-modelcontextprotocol-python-sdk","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-modelcontextprotocol-python-sdk__cap_0","uri":"capability://tool.use.integration.decorator.based.mcp.server.framework.with.automatic.type.inference","name":"decorator-based mcp server framework with automatic type inference","description":"FastMCP provides a high-level decorator-driven API (@mcp.tool(), @mcp.resource(), @mcp.prompt()) that automatically wraps Python function return values into MCP protocol types and injects context via type annotations. Uses Python's inspect module to extract function signatures and Pydantic models to generate JSON schemas for tool parameters, eliminating manual protocol message construction. The framework handles automatic serialization of return values and context injection through type hints, reducing boilerplate from ~50 lines to ~5 lines per tool.","intents":["I want to expose Python functions as MCP tools without manually constructing protocol messages","I need automatic JSON schema generation from my function signatures for LLM tool calling","I want to inject context (like request metadata) into my tool handlers via type annotations"],"best_for":["Python developers building LLM agents quickly","teams prototyping MCP servers without deep protocol knowledge","developers prioritizing developer experience over low-level control"],"limitations":["Abstracts away protocol details, making advanced customization harder","Type annotation-based context injection requires specific type signatures","Limited to Python function signatures — cannot express complex nested schemas without Pydantic models"],"requires":["Python 3.9+","Pydantic 2.0+ for schema generation","FastMCP module from mcp.server.fastmcp"],"input_types":["Python function definitions with type hints","Pydantic models for complex parameter schemas"],"output_types":["MCP Tool, Resource, and Prompt protocol messages","JSON-RPC 2.0 responses with automatic type wrapping"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-python-sdk__cap_1","uri":"capability://tool.use.integration.low.level.handler.based.server.api.with.explicit.protocol.control","name":"low-level handler-based server api with explicit protocol control","description":"The Server class in src/mcp/server/lowlevel/server.py provides constructor-based handler registration (on_list_tools=..., on_call_tool=..., on_read_resource=...) for developers needing fine-grained control over MCP protocol behavior. Handlers receive raw protocol request objects and must explicitly construct Pydantic-validated response types, enabling custom logic for authentication, caching, dynamic tool generation, and protocol negotiation. This low-level API bypasses FastMCP's abstractions and exposes the full JSON-RPC 2.0 message lifecycle.","intents":["I need to implement custom authentication or authorization logic at the protocol level","I want to dynamically generate tools based on runtime state or external APIs","I need to handle edge cases or protocol features not covered by FastMCP decorators"],"best_for":["advanced developers building production MCP servers with custom requirements","teams implementing protocol-level security or caching strategies","developers integrating MCP with existing Python frameworks"],"limitations":["Requires manual construction of all Pydantic response types","No automatic context injection — must manually extract from request objects","Verbose compared to FastMCP; ~50 lines per handler vs ~5 with decorators","Developer must understand full MCP protocol and JSON-RPC 2.0 semantics"],"requires":["Python 3.9+","Pydantic 2.0+ for response type validation","Understanding of MCP protocol messages and JSON-RPC 2.0"],"input_types":["JSON-RPC 2.0 request objects (Pydantic models)","Raw protocol messages with method and params fields"],"output_types":["Pydantic-validated MCP response types","JSON-RPC 2.0 result or error responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-python-sdk__cap_10","uri":"capability://automation.workflow.progress.reporting.and.long.running.operation.notifications","name":"progress reporting and long-running operation notifications","description":"The SDK supports progress reporting for long-running operations through the progress notification mechanism. Servers can send progress updates (progress_start, progress_update, progress_end) to clients during tool execution, allowing clients to display progress bars or status updates. Progress notifications are sent asynchronously without blocking tool execution, enabling real-time feedback for operations that take seconds or minutes to complete.","intents":["I want to send progress updates to clients while my tool is executing a long operation","I need to report completion percentage or status messages during tool execution","I want clients to display progress bars or status updates for long-running tools"],"best_for":["tools that perform long-running operations (file processing, API calls, computations)","developers building interactive MCP clients that display progress","teams that need real-time feedback for operations taking >1 second"],"limitations":["Progress notifications are fire-and-forget — no guarantee of delivery","Client handling of progress notifications is optional — not all clients display progress","Progress reporting adds overhead (~1-2ms per notification) for high-frequency updates","No built-in progress aggregation for nested operations"],"requires":["Python 3.9+","Access to ServerSession or RequestContext to send notifications","Client support for progress notifications (not all MCP clients implement this)"],"input_types":["Progress start/update/end events with percentage and status messages"],"output_types":["Progress notifications sent to connected clients","Real-time progress updates displayed in client UI"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-python-sdk__cap_11","uri":"capability://tool.use.integration.capability.negotiation.and.protocol.version.compatibility","name":"capability negotiation and protocol version compatibility","description":"The SDK implements MCP capability negotiation through the initialize protocol method, where clients and servers exchange supported capabilities (tools, resources, prompts, notifications, etc.). Both sides declare their capabilities, and the protocol layer validates compatibility. This enables forward/backward compatibility: older clients can work with newer servers by ignoring unsupported capabilities, and servers can adapt behavior based on client capabilities.","intents":["I want my MCP server to work with clients that support different protocol versions","I need to know which capabilities a client supports before sending requests","I want to gracefully degrade functionality when clients don't support certain features"],"best_for":["teams deploying MCP servers that need to support multiple client versions","developers building MCP clients that need to adapt to server capabilities","projects where protocol evolution requires backward compatibility"],"limitations":["Capability negotiation happens once at connection time — no dynamic capability changes","No built-in fallback mechanisms — applications must implement graceful degradation","Capability mismatch errors are generic — debugging requires protocol inspection","Complex capability combinations may not be fully validated"],"requires":["Python 3.9+","Understanding of MCP protocol capabilities","Proper implementation of initialize handler"],"input_types":["Client capabilities declaration (tools, resources, prompts, notifications)","Server capabilities declaration"],"output_types":["Negotiated capabilities for the session","Protocol version information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-python-sdk__cap_12","uri":"capability://planning.reasoning.experimental.task.system.for.multi.step.operations","name":"experimental task system for multi-step operations","description":"The SDK includes an experimental task system (src/mcp/types.py) that enables servers to define multi-step operations where clients can submit tasks and receive results asynchronously. 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This is an experimental feature designed for operations that span multiple protocol round-trips or require client-side decision making between steps.","intents":["I want to implement multi-step operations where clients make decisions between steps","I need to support long-running tasks that clients can cancel or monitor","I want to stream results from tasks as they become available"],"best_for":["advanced use cases requiring multi-step operations","developers building interactive MCP applications with client-side decision making","teams experimenting with task-based workflows"],"limitations":["Experimental feature — API may change in future versions","Limited client support — most MCP clients don't implement task handling","Task state management must be implemented by the server","No built-in persistence — tasks are lost if server restarts"],"requires":["Python 3.9+","Understanding of experimental API stability","Client support for task protocol (rare)"],"input_types":["Task definitions with steps and parameters"],"output_types":["Task results and progress updates","Cancellation confirmations"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-python-sdk__cap_13","uri":"capability://data.processing.analysis.structured.output.and.content.type.handling","name":"structured output and content type handling","description":"The SDK supports multiple content types (text, image, PDF, etc.) through a unified TextContent and ImageContent type system. Tool results can return structured content with MIME types, enabling rich output beyond plain text. The protocol layer automatically serializes content based on type, and clients can handle different content types appropriately (display images, render PDFs, etc.). This enables tools to return complex outputs without requiring clients to parse text representations.","intents":["I want my tools to return images, PDFs, or other rich content types","I need to return structured data with proper MIME type information","I want clients to handle different content types appropriately without parsing text"],"best_for":["tools that generate or process images, PDFs, or other binary content","developers building MCP servers with rich output requirements","teams that need proper content type handling for different output formats"],"limitations":["Large binary content (>10MB) may cause serialization overhead","Not all clients support all content types — fallback to text required","Binary content must be base64-encoded for JSON transmission, increasing payload size by ~33%","No built-in compression for large content"],"requires":["Python 3.9+","Pydantic 2.0+ for content type validation","Client support for desired content types"],"input_types":["Text content with optional MIME type","Binary content (images, PDFs) as bytes or base64 strings"],"output_types":["TextContent and ImageContent objects with MIME types","Base64-encoded binary data for transmission"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-python-sdk__cap_2","uri":"capability://automation.workflow.transport.agnostic.session.management.with.multiple.protocol.backends","name":"transport-agnostic session management with multiple protocol backends","description":"The SDK abstracts transport mechanisms (STDIO, SSE, StreamableHTTP) through a uniform (read_stream, write_stream) interface that carries SessionMessage objects, allowing application code to remain transport-agnostic. ServerSession and ClientSession classes manage bidirectional communication, message routing, and lifecycle events independently of the underlying transport. StreamableHTTPSessionManager adds production features: session resumability via event stores, DNS rebinding protection, and stateful session recovery across connection interruptions.","intents":["I want to build an MCP server that works over STDIO, HTTP, and SSE without changing application code","I need session resumability and recovery for production deployments","I want to protect against DNS rebinding attacks in HTTP-based MCP servers"],"best_for":["teams deploying MCP servers across multiple transport protocols","production environments requiring session persistence and recovery","developers building Claude Desktop integrations or cloud-hosted MCP servers"],"limitations":["STDIO transport limited to single-process communication","SSE transport is unidirectional (server-to-client only) for notifications","StreamableHTTP requires external event store for session resumability","Transport abstraction adds ~50-100ms latency per round-trip due to message serialization"],"requires":["Python 3.9+","asyncio for async transport handling","For StreamableHTTP: external event store (e.g., database) for session persistence","For HTTP: HTTPS in production with valid certificates"],"input_types":["SessionMessage objects (JSON-RPC 2.0 wrapped)","Read/write stream pairs from transport implementations"],"output_types":["SessionMessage objects routed to appropriate handlers","Notifications and responses sent back through write streams"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-python-sdk__cap_3","uri":"capability://tool.use.integration.json.rpc.2.0.protocol.implementation.with.pydantic.discriminated.unions","name":"json-rpc 2.0 protocol implementation with pydantic discriminated unions","description":"The SDK implements the full MCP protocol as JSON-RPC 2.0 using Pydantic's discriminated unions (src/mcp/types.py) to automatically route messages based on the 'method' field. All protocol messages (requests, responses, notifications) are defined as Pydantic models with strict validation, enabling type-safe message handling and automatic serialization/deserialization. The discriminated union pattern eliminates manual message routing logic and provides compile-time type checking for protocol compliance.","intents":["I want type-safe JSON-RPC 2.0 message handling with automatic validation","I need to ensure my MCP server/client messages comply with the protocol specification","I want to avoid manual message routing and type casting in my application code"],"best_for":["developers building MCP servers/clients with strict type safety requirements","teams that need protocol compliance validation at runtime","projects where message routing errors should be caught at the type level"],"limitations":["Pydantic validation adds ~5-10ms per message for complex nested types","Discriminated unions require exact 'method' field matching — typos cause validation failures","Custom protocol extensions require modifying the types.py file or creating wrapper models","Large message payloads (>10MB) may cause serialization overhead"],"requires":["Python 3.9+","Pydantic 2.0+ with discriminated union support","Understanding of JSON-RPC 2.0 protocol semantics"],"input_types":["JSON strings or dictionaries representing JSON-RPC 2.0 messages","Python dictionaries with 'method' and 'params' fields"],"output_types":["Pydantic model instances (Request, Response, Notification, Error)","JSON strings for transmission over transports"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-python-sdk__cap_4","uri":"capability://tool.use.integration.client.side.request.response.handling.with.server.notification.subscriptions","name":"client-side request/response handling with server notification subscriptions","description":"ClientSession provides a high-level async API for making MCP requests (list_tools, call_tool, read_resource, etc.) and subscribing to server notifications through callback handlers. The client automatically manages request IDs, correlates responses to requests, and handles both request/response patterns and server-initiated notifications. Built on asyncio, it supports concurrent requests with proper error handling and timeout management, abstracting away JSON-RPC 2.0 request ID correlation.","intents":["I want to call MCP server tools from Python with automatic request/response correlation","I need to subscribe to server notifications and handle them asynchronously","I want a high-level async API that abstracts JSON-RPC 2.0 request ID management"],"best_for":["Python developers building LLM agents that consume MCP servers","teams integrating MCP clients into existing async Python applications","developers who need clean async/await syntax for MCP interactions"],"limitations":["Requires asyncio event loop — not suitable for synchronous-only applications","No built-in request caching — repeated calls to same tool execute fresh requests","Notification subscriptions are in-memory only — no persistence across restarts","Timeout handling is basic; no exponential backoff or retry logic built-in"],"requires":["Python 3.9+","asyncio event loop","Active MCP server connection via transport","Understanding of async/await patterns"],"input_types":["Tool names and parameters (as dictionaries or Pydantic models)","Resource URIs and request parameters","Callback functions for notification handlers"],"output_types":["Tool execution results (text, images, structured data)","Resource content (text, binary, structured)","Notification objects passed to registered callbacks"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-python-sdk__cap_5","uri":"capability://code.generation.editing.function.metadata.extraction.and.json.schema.generation.from.type.hints","name":"function metadata extraction and json schema generation from type hints","description":"The SDK uses Python's inspect module to extract function signatures and Pydantic's schema generation to automatically create JSON schemas for tool parameters. Type hints (int, str, list, Pydantic models) are converted to JSON Schema Draft 7 format, with docstrings parsed for parameter descriptions. This enables LLMs to understand tool parameters without manual schema definition, supporting complex nested types, optional parameters, and default values through Pydantic's validation framework.","intents":["I want to automatically generate JSON schemas for my tool parameters from Python type hints","I need LLMs to understand complex nested parameter types without manual schema writing","I want parameter descriptions extracted from docstrings to appear in tool definitions"],"best_for":["developers building MCP tools with complex parameter types","teams that want to avoid manual JSON schema maintenance","projects where parameter types change frequently and schemas must stay in sync"],"limitations":["Docstring parsing is basic — only supports standard formats (Google, NumPy)","Complex recursive types may generate overly verbose schemas","Union types are flattened to anyOf, which some LLMs handle poorly","Custom types require Pydantic model definitions to generate proper schemas"],"requires":["Python 3.9+","Type hints on all function parameters","Pydantic 2.0+ for schema generation","Docstrings for parameter descriptions (optional but recommended)"],"input_types":["Python function definitions with type hints","Pydantic model classes","Docstrings in Google or NumPy format"],"output_types":["JSON Schema Draft 7 objects","Tool definitions with parameter schemas"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-python-sdk__cap_6","uri":"capability://tool.use.integration.context.injection.via.type.annotations.in.tool.handlers","name":"context injection via type annotations in tool handlers","description":"FastMCP supports context injection by examining function parameter type annotations and automatically providing matching context objects (RequestContext, ServerSession, etc.) at runtime. A tool function can declare parameters like `context: RequestContext` and FastMCP will inject the current request context without requiring explicit parameter passing. This pattern is implemented through Python's inspect module and type annotation introspection, enabling clean separation between business logic and protocol-level context.","intents":["I want to access request metadata (client info, session ID) in my tool handlers without explicit parameters","I need to pass server state or session information to tools without polluting function signatures","I want clean tool functions that focus on business logic while framework handles context plumbing"],"best_for":["developers building tools that need request context or server state","teams that want clean separation between business logic and framework concerns","projects where multiple tools share common context requirements"],"limitations":["Context types must be explicitly supported by FastMCP — custom context types require framework modification","Type annotation introspection adds ~1-2ms overhead per tool invocation","Debugging context injection issues requires understanding the introspection mechanism","IDE autocomplete may not recognize injected parameters without explicit type hints"],"requires":["Python 3.9+","Type hints on context parameters","FastMCP framework (not available in low-level Server API)","Understanding of supported context types (RequestContext, ServerSession, etc.)"],"input_types":["Function parameter type annotations","Supported context types (RequestContext, ServerSession, etc.)"],"output_types":["Context objects injected into function parameters at runtime"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-python-sdk__cap_7","uri":"capability://safety.moderation.server.side.authentication.and.token.based.authorization","name":"server-side authentication and token-based authorization","description":"The SDK supports server-side authentication through token verification and authorization configuration. Servers can register authorization handlers that validate client tokens (JWT, API keys, etc.) before processing requests. The authorization layer integrates with ServerSession to enforce per-session authorization policies, enabling fine-grained access control over tools, resources, and prompts. Authorization failures return JSON-RPC 2.0 error responses with appropriate error codes.","intents":["I want to require clients to authenticate with tokens before accessing my MCP server","I need to enforce authorization policies (e.g., only certain clients can call certain tools)","I want to validate JWT tokens or API keys at the protocol level"],"best_for":["production MCP servers that need to restrict access to sensitive tools/resources","teams integrating MCP with existing authentication systems (OAuth, JWT, API keys)","developers building multi-tenant MCP servers with per-client authorization"],"limitations":["Authorization logic must be implemented by the developer — no built-in JWT validation","Token validation adds latency to every request (~5-10ms for JWT verification)","No built-in token refresh or expiration handling — must be implemented in authorization handler","Authorization decisions are synchronous — async authorization requires custom implementation"],"requires":["Python 3.9+","Token validation library (e.g., PyJWT for JWT)","Authorization handler function that validates tokens","Understanding of authentication/authorization patterns"],"input_types":["Client tokens (JWT, API keys, etc.) in request headers or body","Authorization policies (which clients can access which resources)"],"output_types":["Authorization success/failure responses","JSON-RPC 2.0 error responses with appropriate error codes"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-python-sdk__cap_8","uri":"capability://tool.use.integration.resource.and.prompt.definitions.with.dynamic.content.serving","name":"resource and prompt definitions with dynamic content serving","description":"The SDK supports MCP Resources (static or dynamic content accessible via URI) and Prompts (reusable instruction templates) through decorator-based definitions (@mcp.resource(), @mcp.prompt()). Resources can be static (file paths, URLs) or dynamic (generated at request time from handler functions). Prompts support template variables that LLMs can fill in. Both are registered with the server and advertised to clients through list_resources and list_prompts protocol methods, enabling clients to discover and consume them.","intents":["I want to expose files, URLs, or dynamically-generated content as MCP resources","I need to provide reusable instruction templates (prompts) that LLMs can use","I want clients to discover available resources and prompts without hardcoding URIs"],"best_for":["developers building MCP servers that serve content (documentation, data, templates)","teams creating prompt libraries for LLM applications","projects where resources or prompts change dynamically based on server state"],"limitations":["Resource content is served synchronously — large files may block the event loop","No built-in caching for resource content — repeated requests re-generate content","Prompt templates use simple variable substitution — no complex logic or conditionals","Resource URIs must be unique — no namespace support for organizing resources"],"requires":["Python 3.9+","FastMCP framework for decorator-based definitions","For file resources: file paths accessible to the server process","For dynamic resources: handler functions that generate content"],"input_types":["File paths or URLs for static resources","Handler functions for dynamic resources","Template strings with variable placeholders for prompts"],"output_types":["Resource content (text, binary, structured data)","Prompt definitions with template variables","Resource and prompt listings for client discovery"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-python-sdk__cap_9","uri":"capability://automation.workflow.lifespan.management.and.server.initialization.shutdown.hooks","name":"lifespan management and server initialization/shutdown hooks","description":"FastMCP supports lifespan management through @mcp.server.lifespan context managers that execute initialization and cleanup code when the server starts and stops. The lifespan pattern (async context manager) allows servers to set up resources (database connections, thread pools, caches) on startup and clean them up on shutdown. This integrates with the server lifecycle, ensuring resources are properly released even if the server crashes or is forcefully terminated.","intents":["I want to initialize database connections or other resources when my MCP server starts","I need to clean up resources (close connections, flush caches) when the server shuts down","I want to ensure cleanup happens even if the server crashes or is forcefully terminated"],"best_for":["servers that manage stateful resources (databases, connections, caches)","teams that need guaranteed cleanup of resources on shutdown","developers building production MCP servers with resource management requirements"],"limitations":["Lifespan context managers are async-only — synchronous resource initialization requires wrapping","No timeout enforcement on cleanup — hung cleanup code can delay server shutdown","Cleanup is best-effort; forceful process termination (SIGKILL) bypasses cleanup","Multiple lifespan managers must be manually composed — no automatic dependency ordering"],"requires":["Python 3.9+","asyncio event loop","Understanding of async context managers","FastMCP framework"],"input_types":["Async context manager functions with setup/teardown logic"],"output_types":["Initialized resources available to tool handlers","Cleanup execution on server shutdown"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Pydantic 2.0+ for schema generation","FastMCP module from mcp.server.fastmcp","Pydantic 2.0+ for response type validation","Understanding of MCP protocol messages and JSON-RPC 2.0","Access to ServerSession or RequestContext to send notifications","Client support for progress notifications (not all MCP clients implement this)","Understanding of MCP protocol capabilities","Proper implementation of initialize handler","Understanding of experimental API stability"],"failure_modes":["Abstracts away protocol details, making advanced customization harder","Type annotation-based context injection requires specific type signatures","Limited to Python function signatures — cannot express complex nested schemas without Pydantic models","Requires manual construction of all Pydantic response types","No automatic context injection — must manually extract from request objects","Verbose compared to FastMCP; ~50 lines per handler vs ~5 with decorators","Developer must understand full MCP protocol and JSON-RPC 2.0 semantics","Progress notifications are fire-and-forget — no guarantee of delivery","Client handling of progress notifications is optional — not all clients display progress","Progress reporting adds overhead (~1-2ms per notification) for high-frequency updates","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7589028428327954,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:34.856Z","last_commit":"2026-05-01T19:45:37Z"},"community":{"stars":22860,"forks":3376,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-modelcontextprotocol-python-sdk","compare_url":"https://unfragile.ai/compare?artifact=mcp-modelcontextprotocol-python-sdk"}},"signature":"xOwf/gbzf7XdVkplE/qd5fK4T5L5CE2dLifZSqlLNqDQPDVlc/7ZcuY/w9ryNdg+uEYp5ARO1ZzHoqt0Nk44Dg==","signedAt":"2026-06-19T20:39:27.858Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-modelcontextprotocol-python-sdk","artifact":"https://unfragile.ai/mcp-modelcontextprotocol-python-sdk","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-modelcontextprotocol-python-sdk","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}