{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-agentr-universal-mcp-sdk","slug":"agentr-universal-mcp-sdk","name":"AgentR Universal MCP SDK","type":"mcp","url":"https://github.com/universal-mcp/universal-mcp","page_url":"https://unfragile.ai/agentr-universal-mcp-sdk","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-agentr-universal-mcp-sdk__cap_0","uri":"capability://tool.use.integration.mcp.server.scaffolding.with.python.decorators","name":"mcp server scaffolding with python decorators","description":"Provides a Python-native decorator-based framework for building Model Context Protocol servers without boilerplate. Uses Python decorators (@mcp_tool, @mcp_resource) to register server capabilities, automatically handling protocol serialization, message routing, and lifecycle management. Abstracts away low-level MCP protocol details while maintaining full protocol compliance.","intents":["I want to quickly build an MCP server without writing protocol boilerplate","I need to expose Python functions as MCP tools to Claude and other LLM clients","I want to define resources and prompts declaratively in Python"],"best_for":["Python developers building LLM-integrated tools and agents","Teams migrating from REST APIs to MCP for LLM integration","Developers prototyping MCP servers without deep protocol knowledge"],"limitations":["Python-only implementation — no native support for Node.js or other runtimes","Decorator-based approach may have performance overhead for high-throughput servers (estimated ~5-10ms per request)","Limited built-in async/await patterns compared to native async frameworks"],"requires":["Python 3.8+","MCP protocol understanding (basic)","pip or poetry for dependency management"],"input_types":["Python function signatures","Type hints (Pydantic models, native types)","Docstrings for tool descriptions"],"output_types":["MCP-compliant server binary","Protocol messages (JSON-RPC 2.0)","Tool/resource definitions"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentr-universal-mcp-sdk__cap_1","uri":"capability://safety.moderation.inbuilt.credential.management.and.secret.injection","name":"inbuilt credential management and secret injection","description":"Provides a built-in credential store and injection system that securely manages API keys, tokens, and secrets for MCP servers without requiring external secret management infrastructure. Uses environment variable detection, credential caching, and optional encryption to inject secrets into tool execution contexts. Integrates with common auth patterns (OAuth, API keys, bearer tokens) and supports credential scoping per tool or resource.","intents":["I need to securely pass API credentials to my MCP tools without hardcoding them","I want my MCP server to work with multiple credential providers (OpenAI, Anthropic, custom APIs)","I need to rotate or update credentials without redeploying my server"],"best_for":["Teams deploying MCP servers in production with sensitive credentials","Developers building multi-tenant MCP services requiring per-user credential isolation","Organizations avoiding external secret management systems (Vault, AWS Secrets Manager)"],"limitations":["Credential encryption is optional and not enforced by default — requires explicit configuration","No built-in audit logging for credential access — requires external monitoring","Limited support for dynamic credential refresh (e.g., OAuth token rotation) — manual refresh required","Credential scoping is tool-level only, not request-level or user-level"],"requires":["Python 3.8+","Environment variables or credential file (JSON/YAML format)","Optional: encryption library (cryptography) for at-rest encryption"],"input_types":["Environment variables","Credential files (JSON, YAML, .env)","API key strings","OAuth tokens"],"output_types":["Injected credentials in tool execution context","Credential metadata (expiry, scope)","Validation status (valid/expired/invalid)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentr-universal-mcp-sdk__cap_10","uri":"capability://code.generation.editing.testing.utilities.and.mock.llm.client","name":"testing utilities and mock llm client","description":"Provides testing utilities including a mock LLM client for unit testing MCP servers without external dependencies. 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Enables fast, deterministic testing of MCP server logic without network calls or real LLM API usage.","intents":["I want to unit test my MCP tools without calling real APIs","I need to test tool behavior with specific LLM responses","I want to mock external dependencies (databases, APIs) in my tests"],"best_for":["Teams building MCP servers with comprehensive test coverage","Developers practicing test-driven development (TDD)","Projects requiring fast, deterministic test execution"],"limitations":["Mock LLM client doesn't replicate all LLM behaviors — edge cases may not be caught","Testing async tools requires async test framework (pytest-asyncio) — adds complexity","Mocking external APIs requires manual setup — no automatic mock generation","Test utilities are framework-specific — limited to pytest (no unittest support)"],"requires":["Python 3.8+","pytest testing framework","Optional: pytest-asyncio for async tool testing"],"input_types":["MCP server instance","Tool definitions","Mock LLM responses","External API mocks"],"output_types":["Test results (pass/fail)","Assertion errors with context","Coverage reports"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentr-universal-mcp-sdk__cap_11","uri":"capability://text.generation.language.documentation.generation.from.tool.definitions","name":"documentation generation from tool definitions","description":"Automatically generates API documentation (Markdown, HTML, OpenAPI) from MCP tool definitions, resource descriptions, and docstrings. 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Handles provider-specific protocol differences (function calling schemas, message formats, streaming behavior) transparently, allowing the same MCP server to work with any supported LLM without code changes. Includes automatic schema translation and response normalization.","intents":["I want my MCP server to work with Claude, GPT-4, and open-source models without rewriting code","I need to switch LLM providers without changing my tool definitions","I want to test my MCP tools against multiple LLM backends"],"best_for":["Teams building LLM applications requiring provider flexibility","Developers prototyping with multiple LLM models simultaneously","Organizations with multi-cloud or hybrid LLM strategies"],"limitations":["Schema translation adds ~50-100ms latency per request due to format conversion","Advanced provider-specific features (vision, structured output) may not be fully abstracted","Streaming behavior differs across providers — normalization may introduce buffering delays","Rate limiting and quota management are provider-specific and not unified"],"requires":["Python 3.8+","API keys for target LLM providers (OpenAI, Anthropic, etc.)","Network access to provider endpoints or local Ollama instance"],"input_types":["LLM provider configuration (API key, endpoint URL, model name)","Tool definitions (MCP schema)","User prompts and messages"],"output_types":["Normalized LLM responses","Tool call requests (unified format)","Streaming tokens or complete responses"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentr-universal-mcp-sdk__cap_3","uri":"capability://code.generation.editing.tool.definition.with.type.validation.and.schema.generation","name":"tool definition with type validation and schema generation","description":"Automatically generates MCP-compliant tool schemas from Python function signatures and type hints (Pydantic models, native types). Validates input arguments against schemas at runtime, providing type safety and automatic OpenAPI/JSON Schema generation. Supports complex nested types, optional parameters, and default values with minimal boilerplate.","intents":["I want to define tools with type safety without writing JSON schemas manually","I need automatic validation of tool arguments before execution","I want to generate OpenAPI documentation from my MCP tools"],"best_for":["Python developers familiar with type hints and Pydantic","Teams requiring strict input validation for tool execution","Projects needing automatic API documentation generation"],"limitations":["Requires Python type hints — untyped functions fall back to basic validation","Pydantic v1 and v2 have different schema generation — version compatibility issues possible","Complex recursive types may generate overly verbose schemas","Validation errors are Python exceptions — require custom error handling for LLM-friendly messages"],"requires":["Python 3.8+","Pydantic 1.10+ or 2.0+","Type hints on all tool functions"],"input_types":["Python function signatures with type hints","Pydantic BaseModel definitions","Native Python types (str, int, list, dict, etc.)"],"output_types":["JSON Schema (MCP-compliant)","OpenAPI 3.0 schema","Validation errors with field-level details"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentr-universal-mcp-sdk__cap_4","uri":"capability://memory.knowledge.resource.and.prompt.definition.with.dynamic.content","name":"resource and prompt definition with dynamic content","description":"Enables declarative definition of MCP resources (documents, files, data) and prompts (system instructions, few-shot examples) with support for dynamic content generation. Resources can be static files, generated on-demand, or streamed from external sources. Prompts support templating and variable substitution, allowing LLMs to access contextual information without embedding it in every request.","intents":["I want to expose files and documents as MCP resources that Claude can read","I need to provide dynamic prompts that change based on context or user input","I want to stream large documents to LLMs without loading them fully into memory"],"best_for":["Teams building knowledge-augmented LLM applications","Developers creating document-aware AI agents","Projects requiring dynamic system prompts or few-shot examples"],"limitations":["Resource streaming requires client support — not all LLM clients handle streaming resources","Dynamic content generation adds latency (100-500ms depending on source)","No built-in caching for frequently-accessed resources — requires external cache layer","Templating is basic (variable substitution only) — no conditional logic or loops"],"requires":["Python 3.8+","File system access or external data source (API, database)","MCP client supporting resource streaming (Claude, some custom clients)"],"input_types":["Static file paths","Python functions returning resource content","Template strings with variable placeholders","External API endpoints"],"output_types":["MCP resource definitions (URI, MIME type, content)","Streamed content (text, binary)","Rendered prompts with substituted variables"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentr-universal-mcp-sdk__cap_5","uri":"capability://automation.workflow.server.lifecycle.management.and.graceful.shutdown","name":"server lifecycle management and graceful shutdown","description":"Handles MCP server startup, shutdown, and resource cleanup through lifecycle hooks (on_startup, on_shutdown). Manages connection pooling, credential caching, and external resource cleanup automatically. Supports graceful shutdown with timeout-based force termination, ensuring no in-flight requests are lost and all resources are properly released.","intents":["I want my MCP server to initialize resources (database connections, API clients) on startup","I need to clean up resources and close connections when the server shuts down","I want to handle server restarts without losing in-flight requests"],"best_for":["Production MCP deployments requiring resource management","Teams running MCP servers in containerized environments (Docker, Kubernetes)","Applications with stateful resources (database connections, file handles)"],"limitations":["Lifecycle hooks are blocking — long-running initialization delays server startup","No built-in health check endpoints — requires external monitoring","Graceful shutdown timeout is fixed — not configurable per deployment","Resource cleanup is best-effort — no guarantee of cleanup completion"],"requires":["Python 3.8+","Async/await support for non-blocking lifecycle hooks","Signal handling (SIGTERM, SIGINT) for graceful shutdown"],"input_types":["Lifecycle hook functions (async or sync)","Resource initialization parameters","Shutdown timeout configuration"],"output_types":["Server startup/shutdown logs","Resource cleanup status","Error messages for failed cleanup"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentr-universal-mcp-sdk__cap_6","uri":"capability://automation.workflow.async.await.support.for.non.blocking.tool.execution","name":"async/await support for non-blocking tool execution","description":"Enables async Python functions as MCP tools, allowing non-blocking I/O operations (API calls, database queries, file operations) without blocking the server. Automatically handles async context management, concurrent tool execution, and error propagation. Supports both sync and async tools in the same server with transparent execution model.","intents":["I want my MCP tools to make API calls without blocking other requests","I need to run multiple tools concurrently when the LLM calls them in parallel","I want to use async libraries (aiohttp, asyncpg) in my MCP tools"],"best_for":["High-throughput MCP servers handling multiple concurrent requests","Tools making external API calls or database queries","Teams using async Python libraries (FastAPI, aiohttp, asyncpg)"],"limitations":["Mixing sync and async tools adds complexity — requires careful event loop management","Async debugging is harder than sync code — stack traces are less readable","Some Python libraries don't have async equivalents — requires sync wrappers or thread pools","Concurrent execution limits depend on system resources — no built-in rate limiting"],"requires":["Python 3.8+","Understanding of async/await and event loops","Async-compatible libraries for external I/O"],"input_types":["Async Python functions","Sync Python functions (automatically wrapped)","Concurrent execution requests from LLM"],"output_types":["Tool results (same as sync tools)","Execution timing metadata","Error messages with async stack traces"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentr-universal-mcp-sdk__cap_7","uri":"capability://safety.moderation.error.handling.and.exception.propagation.to.llm.clients","name":"error handling and exception propagation to llm clients","description":"Provides structured error handling that converts Python exceptions into MCP-compliant error responses with meaningful messages for LLM clients. Supports custom error types, error context preservation, and automatic error logging. Distinguishes between recoverable errors (retry-able) and fatal errors (non-retry-able) to guide LLM behavior.","intents":["I want tool errors to be reported clearly to the LLM so it can retry or handle them","I need to distinguish between temporary failures (network timeout) and permanent failures (invalid input)","I want to log errors for debugging without exposing sensitive details to the LLM"],"best_for":["Production MCP servers requiring robust error handling","Teams building resilient LLM agents that handle tool failures gracefully","Applications with external dependencies (APIs, databases) prone to transient failures"],"limitations":["Error messages are exposed to LLM clients — requires sanitization to avoid leaking sensitive info","Retry logic is LLM-side — server cannot force retries, only suggest them","Stack traces are not sent to clients — requires separate logging for debugging","Custom error types require explicit registration — no automatic discovery"],"requires":["Python 3.8+","Custom exception classes (optional but recommended)","Logging configuration for error capture"],"input_types":["Python exceptions (built-in and custom)","Error context (tool name, arguments, execution time)","Retry metadata (is_retryable, retry_after)"],"output_types":["MCP error responses (JSON-RPC error format)","Error messages for LLM consumption","Server-side error logs"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentr-universal-mcp-sdk__cap_8","uri":"capability://automation.workflow.configuration.management.with.environment.variable.support","name":"configuration management with environment variable support","description":"Provides centralized configuration management for MCP servers with environment variable detection, type coercion, and validation. Supports configuration files (YAML, JSON, .env) and environment variable overrides, with automatic fallback to defaults. Enables easy deployment across different environments (dev, staging, prod) without code changes.","intents":["I want to configure my MCP server with environment variables for different deployments","I need to manage API keys, endpoints, and feature flags without hardcoding them","I want to validate configuration on startup to catch errors early"],"best_for":["Teams deploying MCP servers across multiple environments","Containerized deployments (Docker, Kubernetes) using environment variables","Projects requiring feature flags or dynamic configuration"],"limitations":["Configuration validation is basic — complex validation logic requires custom code","No built-in hot-reload — configuration changes require server restart","Environment variable naming conventions must be followed — no automatic conversion","Type coercion is limited to basic types (str, int, bool, list) — complex types require custom parsing"],"requires":["Python 3.8+","Environment variables or configuration files in supported format","Optional: python-dotenv for .env file support"],"input_types":["Environment variables","Configuration files (YAML, JSON, .env)","Default configuration values"],"output_types":["Parsed configuration object","Validation errors with field details","Configuration metadata (source, type)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentr-universal-mcp-sdk__cap_9","uri":"capability://automation.workflow.logging.and.observability.integration","name":"logging and observability integration","description":"Integrates structured logging with support for multiple log levels, formatters, and handlers. Provides built-in metrics collection (request count, latency, error rate) and integration points for external observability platforms (Datadog, New Relic, Prometheus). Logs all tool invocations with context (tool name, arguments, execution time, result) for debugging and monitoring.","intents":["I want to see detailed logs of tool execution for debugging","I need to monitor MCP server performance and error rates in production","I want to integrate my MCP server with external monitoring platforms"],"best_for":["Production MCP deployments requiring observability","Teams using centralized logging platforms (ELK, Datadog, CloudWatch)","Applications requiring performance monitoring and alerting"],"limitations":["Logging adds ~5-10ms overhead per request — may impact latency-sensitive applications","Structured logging requires consistent field naming — requires discipline across team","Metrics collection is in-memory only — no built-in persistence or aggregation","Integration with external platforms requires custom code — not all platforms supported out-of-box"],"requires":["Python 3.8+","Python logging module (built-in)","Optional: external observability SDKs (datadog, prometheus_client, etc.)"],"input_types":["Tool invocation context (name, arguments, execution time)","Server events (startup, shutdown, errors)","Custom metrics and events"],"output_types":["Structured log entries (JSON format)","Metrics (counters, histograms, gauges)","Integration with external platforms"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","MCP protocol understanding (basic)","pip or poetry for dependency management","Environment variables or credential file (JSON/YAML format)","Optional: encryption library (cryptography) for at-rest encryption","pytest testing framework","Optional: pytest-asyncio for async tool testing","Tool definitions with docstrings","Optional: Jinja2 for custom templates","API keys for target LLM providers (OpenAI, Anthropic, etc.)"],"failure_modes":["Python-only implementation — no native support for Node.js or other runtimes","Decorator-based approach may have performance overhead for high-throughput servers (estimated ~5-10ms per request)","Limited built-in async/await patterns compared to native async frameworks","Credential encryption is optional and not enforced by default — requires explicit configuration","No built-in audit logging for credential access — requires external monitoring","Limited support for dynamic credential refresh (e.g., OAuth token rotation) — manual refresh required","Credential scoping is tool-level only, not request-level or user-level","Mock LLM client doesn't replicate all LLM behaviors — edge cases may not be caught","Testing async tools requires async test framework (pytest-asyncio) — adds complexity","Mocking external APIs requires manual setup — no automatic mock generation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"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":"2026-06-17T09:51:02.370Z","last_scraped_at":"2026-05-03T14:00:18.053Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=agentr-universal-mcp-sdk","compare_url":"https://unfragile.ai/compare?artifact=agentr-universal-mcp-sdk"}},"signature":"O6kW1sIf9aw0BU7ExU1jqryJc/r8RlRbR4Y0wCO4COnMy+CxGDR2UIJu2VY8h/IAHZ26F1mbtnliT0P0VAmACA==","signedAt":"2026-06-22T05:56:53.482Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agentr-universal-mcp-sdk","artifact":"https://unfragile.ai/agentr-universal-mcp-sdk","verify":"https://unfragile.ai/api/v1/verify?slug=agentr-universal-mcp-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"}}