{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-mcp-use-mcp-use","slug":"mcp-mcp-use-mcp-use","name":"mcp-use","type":"mcp","url":"https://github.com/mcp-use/mcp-use","page_url":"https://unfragile.ai/mcp-mcp-use-mcp-use","categories":["mcp-servers"],"tags":["agentic-framework","ai","apps-sdk","chatgpt","claude-code","llms","mcp","mcp-apps","mcp-client","mcp-gateway","mcp-host","mcp-inspector","mcp-server","mcp-servers","mcp-tools","mcp-ui","model-context-protocol","modelcontextprotocol","openclaw","skills"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-mcp-use-mcp-use__cap_0","uri":"capability://planning.reasoning.mcp.agent.orchestration.with.multi.step.reasoning","name":"mcp agent orchestration with multi-step reasoning","description":"Enables building autonomous AI agents that decompose complex tasks into sequential steps using MCP tools. The MCPAgent class (available in both Python and TypeScript) manages tool discovery, invocation, and result aggregation across multiple MCP servers, with built-in support for streaming responses and structured output. Agents maintain conversation context and can reason across tool calls to accomplish multi-step objectives.","intents":["Build an autonomous agent that can call multiple tools sequentially to solve a user query","Create a reasoning loop where an LLM decides which MCP tools to invoke based on intermediate results","Implement multi-turn agent interactions with memory of previous tool calls and results"],"best_for":["Teams building agentic AI applications that need to orchestrate multiple external tools","Developers creating autonomous workflows that require multi-step planning and execution","Organizations integrating Claude or ChatGPT with custom tool ecosystems"],"limitations":["Streaming and structured output require explicit configuration; default behavior may not preserve all response metadata","Multi-server coordination adds latency proportional to number of concurrent tool calls","Python and TypeScript implementations have feature parity gaps in advanced observability features"],"requires":["Python 3.8+ (Python SDK) or Node.js 18+ (TypeScript SDK)","At least one MCP server running and accessible via stdio or HTTP transport","LLM API key (OpenAI, Anthropic, or compatible provider) for agent reasoning"],"input_types":["natural language queries","structured task specifications","conversation history with context"],"output_types":["tool invocation results","streaming text responses","structured JSON output from tools"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mcp-use-mcp-use__cap_1","uri":"capability://tool.use.integration.mcp.client.programmatic.tool.invocation","name":"mcp client programmatic tool invocation","description":"Provides a synchronous and asynchronous client interface (MCPClient) for directly calling MCP server tools without LLM intermediation. The client handles connection management, tool discovery via MCP's list_tools protocol, parameter validation against tool schemas, and result parsing. Supports both stdio and HTTP transports with automatic reconnection and error handling.","intents":["Programmatically invoke MCP tools from Python or TypeScript code without an LLM","Build deterministic workflows that call specific tools with known parameters","Integrate MCP servers into non-agentic applications (CLIs, scripts, backend services)"],"best_for":["Backend developers building service-to-service integrations via MCP","DevOps teams automating infrastructure tasks using MCP tool servers","Non-technical users building workflows via low-code platforms that expose MCP clients"],"limitations":["No built-in retry logic or circuit breaker; requires external orchestration for fault tolerance","Synchronous Python client blocks on I/O; async client requires asyncio event loop management","Tool schema validation happens at call time, not parse time; invalid parameters fail at runtime"],"requires":["Python 3.8+ with mcp-use package or Node.js 18+ with @mcp-use/client","Running MCP server accessible via stdio, HTTP, or SSE transport","Knowledge of target tool names and parameter schemas"],"input_types":["tool name (string)","tool arguments (JSON-serializable dict/object)","connection configuration (host, port, transport type)"],"output_types":["tool execution results (JSON or text)","error objects with diagnostic information","streaming results for long-running tools"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mcp-use-mcp-use__cap_10","uri":"capability://automation.workflow.configuration.driven.server.and.deployment.management","name":"configuration-driven server and deployment management","description":"Supports declarative configuration (YAML/JSON) for defining MCP servers, connectors, and deployment parameters without code changes. Configuration files specify server definitions (name, type, transport, executable path), authentication credentials, resource limits, and deployment targets. Framework loads configuration at runtime and instantiates servers/connectors accordingly, enabling environment-specific configurations.","intents":["Define MCP server topology in configuration files for easy environment switching","Manage authentication credentials and secrets through configuration without hardcoding","Deploy MCP infrastructure to different environments (dev, staging, prod) with configuration changes only"],"best_for":["DevOps teams managing MCP infrastructure across multiple environments","Organizations requiring configuration-driven deployment without code changes","Teams using infrastructure-as-code practices for MCP server management"],"limitations":["Configuration schema is fixed; complex deployment scenarios may require code customization","No built-in secret management; requires external secret store integration","Configuration validation happens at runtime; invalid configs fail during initialization"],"requires":["Python 3.8+ or Node.js 18+","Configuration file in YAML or JSON format","Environment variables or secret store for sensitive credentials"],"input_types":["YAML/JSON configuration files","environment variables for secret injection","command-line overrides"],"output_types":["instantiated server/connector objects","validated configuration with defaults applied","deployment manifests (optional)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mcp-use-mcp-use__cap_11","uri":"capability://safety.moderation.sandboxed.execution.environment.for.tool.invocation","name":"sandboxed execution environment for tool invocation","description":"Provides optional sandboxing for tool execution to isolate untrusted code and limit resource access. Sandboxing can restrict file system access, network calls, and CPU/memory usage through OS-level mechanisms (containers, seccomp, resource limits). Framework provides configuration options to enable/disable sandboxing per tool or globally.","intents":["Safely execute untrusted MCP tools without risking system compromise","Limit resource consumption (CPU, memory, disk) for runaway tool processes","Implement security policies that restrict tool access to specific files or network endpoints"],"best_for":["Organizations running user-provided or third-party MCP tools","Multi-tenant systems requiring isolation between tool executions","Security-sensitive applications requiring defense-in-depth for tool execution"],"limitations":["Sandboxing adds significant overhead (container startup, resource limits enforcement); not suitable for latency-sensitive applications","Sandbox escape vulnerabilities possible; sandboxing is defense-in-depth, not complete security guarantee","Requires OS-level support (Linux containers, macOS/Windows virtualization); not available in all environments"],"requires":["Linux kernel with container support (cgroups, namespaces) or Docker/Podman installed","Configuration specifying sandbox type (container, seccomp, resource limits)","Tool definitions with resource requirements (CPU, memory, disk)"],"input_types":["tool execution request with parameters","sandbox configuration (resource limits, file system mounts, network policies)"],"output_types":["tool execution result","resource usage metrics (CPU, memory, disk)","sandbox violation alerts (if applicable)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mcp-use-mcp-use__cap_12","uri":"capability://safety.moderation.authentication.and.credential.management.for.mcp.servers","name":"authentication and credential management for mcp servers","description":"Provides mechanisms for authenticating to MCP servers and managing credentials (API keys, OAuth tokens, basic auth). Framework supports multiple authentication schemes (API key headers, OAuth 2.0, mTLS) with credential injection from environment variables or secret stores. Authentication is configured per server and applied automatically to all requests.","intents":["Authenticate to MCP servers that require API keys or OAuth tokens","Manage credentials securely without hardcoding in application code","Support multiple authentication schemes for different MCP servers"],"best_for":["Teams integrating with authenticated MCP servers in production","Organizations requiring secure credential management across environments","Multi-tenant systems with per-tenant authentication credentials"],"limitations":["No built-in credential rotation; requires external secret management system","OAuth 2.0 support is limited to specific flows; custom flows require manual implementation","Credentials are in-memory; no encryption at rest"],"requires":["Python 3.8+ or Node.js 18+","Authentication credentials (API keys, OAuth tokens, certificates)","Environment variables or secret store for credential injection"],"input_types":["authentication scheme (api_key, oauth2, basic_auth, mtls)","credential values (from environment or secret store)","server configuration with auth settings"],"output_types":["authenticated HTTP/stdio connections to MCP servers","credential injection into request headers or connection parameters"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mcp-use-mcp-use__cap_13","uri":"capability://automation.workflow.observability.and.telemetry.collection","name":"observability and telemetry collection","description":"Integrates observability hooks throughout agent execution for collecting metrics, traces, and logs. Framework emits telemetry events for tool invocations, LLM calls, errors, and performance metrics. Telemetry can be exported to standard backends (OpenTelemetry, Datadog, CloudWatch) through pluggable exporters. Includes built-in metrics for latency, token usage, and error rates.","intents":["Monitor agent performance and identify bottlenecks in tool execution","Track LLM token usage and costs across agent interactions","Debug agent failures by examining detailed execution traces and logs"],"best_for":["Teams operating production agent systems requiring observability","Organizations tracking LLM costs and optimizing token usage","Developers debugging complex multi-step agent reasoning"],"limitations":["Telemetry collection adds latency proportional to exporter overhead","Telemetry data volume can be large for high-frequency tool invocations; requires sampling for cost control","Exporter configuration is required; no default telemetry backend"],"requires":["Python 3.8+ or Node.js 18+","Telemetry backend (OpenTelemetry collector, Datadog, CloudWatch, etc.)","Exporter configuration and credentials"],"input_types":["telemetry events (tool invocation, LLM call, error)","exporter configuration (backend type, credentials, sampling rate)"],"output_types":["metrics (latency, token count, error rate)","traces (execution flow with timing)","logs (structured events with context)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mcp-use-mcp-use__cap_14","uri":"capability://code.generation.editing.code.execution.mode.for.dynamic.tool.invocation","name":"code execution mode for dynamic tool invocation","description":"Enables agents to generate and execute code (Python or JavaScript) dynamically to accomplish tasks, with sandboxed execution for safety. Code execution mode allows agents to write custom scripts that invoke MCP tools, process results, and make decisions without predefined tool schemas. Execution environment has access to tool libraries and can import standard libraries.","intents":["Allow agents to write custom code to solve problems not covered by predefined tools","Enable complex data transformations and multi-step logic without tool composition","Support agents in learning and adapting to new problem domains through code generation"],"best_for":["Research and experimentation with agentic AI capabilities","Specialized domains where predefined tools are insufficient","Applications requiring maximum agent flexibility and adaptability"],"limitations":["Code execution adds significant latency and security risk; not suitable for production systems","Generated code quality depends on LLM reasoning; no guarantees of correctness or efficiency","Sandboxing overhead is substantial; execution is slow compared to direct tool invocation"],"requires":["Python 3.8+ or Node.js 18+","Sandboxed execution environment (container, VM, or seccomp)","Standard library access for code execution"],"input_types":["agent reasoning output (generated code)","tool library imports and bindings","execution context (variables, state)"],"output_types":["code execution results","stdout/stderr from executed code","errors and exceptions"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mcp-use-mcp-use__cap_2","uri":"capability://tool.use.integration.mcp.server.creation.with.tool.resource.and.prompt.definitions","name":"mcp server creation with tool, resource, and prompt definitions","description":"Enables building custom MCP servers that expose tools, resources, and prompts to LLMs and clients. The TypeScript SDK provides decorators and class-based patterns for defining server capabilities, with automatic schema generation and protocol compliance. Servers handle incoming MCP requests, execute handler functions, and return results with proper error serialization. Supports both stdio and HTTP server modes for deployment flexibility.","intents":["Create a custom MCP server that exposes proprietary tools to Claude or ChatGPT","Build a resource server that provides LLMs with access to internal knowledge bases or APIs","Develop a prompt server that offers domain-specific system prompts and few-shot examples"],"best_for":["Enterprise teams exposing internal APIs and tools to LLM applications","SaaS providers building MCP integrations for their platforms","Open-source maintainers creating reusable MCP tool servers for the community"],"limitations":["TypeScript-only implementation; Python SDK does not support server creation (client and agent only)","No built-in authentication or authorization; requires custom middleware for access control","Streaming responses require explicit handler implementation; not automatically inferred from function signatures"],"requires":["Node.js 18+ with TypeScript 5.0+","@mcp-use/server package from npm","Understanding of MCP protocol (tools, resources, prompts, transport)"],"input_types":["tool definitions (name, description, input schema)","resource URIs and content templates","prompt specifications with arguments"],"output_types":["MCP protocol-compliant JSON responses","tool execution results","resource content (text, binary, or structured data)","prompt templates with variable substitution"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mcp-use-mcp-use__cap_3","uri":"capability://tool.use.integration.multi.server.session.management.and.connector.abstraction","name":"multi-server session management and connector abstraction","description":"Manages connections to multiple MCP servers through a unified connector interface, handling server lifecycle (startup, shutdown, reconnection), capability discovery, and request routing. The Python SDK's Connectors and Sessions layer abstracts transport details (stdio, HTTP, SSE) and provides automatic server process management for local servers. Sessions maintain state across multiple tool invocations and support concurrent server interactions.","intents":["Connect to multiple MCP servers and coordinate tool calls across them","Automatically start and manage MCP server processes without manual process management","Switch between local and remote MCP servers transparently based on configuration"],"best_for":["Developers building applications that aggregate tools from multiple MCP servers","Teams deploying MCP infrastructure with dynamic server discovery and lifecycle management","Organizations running both local development and cloud-hosted MCP servers"],"limitations":["Server process management (stdio transport) requires OS-level process spawning; not available in sandboxed environments","No built-in load balancing or failover across multiple instances of the same server","Session state is in-memory; no persistence layer for recovery after process restart"],"requires":["Python 3.8+ with mcp-use package","MCP server executables or HTTP endpoints for each server to connect to","Configuration file (YAML or JSON) specifying server definitions and transport details"],"input_types":["server configuration (name, type, transport, executable path or URL)","connection parameters (environment variables, authentication tokens)","session initialization options"],"output_types":["session objects with connected servers","server capability lists (tools, resources, prompts)","aggregated tool results from multiple servers"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mcp-use-mcp-use__cap_4","uri":"capability://tool.use.integration.mcp.inspector.interactive.debugging.and.testing.ui","name":"mcp inspector interactive debugging and testing ui","description":"Provides a web-based UI for discovering, testing, and debugging MCP servers in real-time. The Inspector connects to running MCP servers, displays available tools/resources/prompts with their schemas, allows manual tool invocation with parameter input, and shows request/response logs. Built as a standalone TypeScript application that can be launched via CLI, supporting both local and remote server inspection.","intents":["Interactively test MCP server tools during development without writing client code","Inspect tool schemas and documentation to understand server capabilities","Debug tool invocation failures by examining request/response payloads and error messages"],"best_for":["MCP server developers validating tool definitions and behavior","Integration engineers testing MCP server connectivity and compatibility","Non-technical users exploring MCP server capabilities via UI"],"limitations":["UI-only tool; no programmatic API for automated testing or CI/CD integration","Limited to single server inspection at a time; no multi-server comparison view","Request/response logs are in-memory; no persistence or export functionality"],"requires":["Node.js 18+ with @mcp-use/inspector package installed","Running MCP server accessible via stdio or HTTP","Modern web browser (Chrome, Firefox, Safari, Edge)"],"input_types":["MCP server connection details (stdio command or HTTP URL)","tool parameters (form inputs matching tool schema)"],"output_types":["rendered tool schemas and documentation","tool invocation results (JSON or formatted text)","request/response logs with timestamps"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mcp-use-mcp-use__cap_5","uri":"capability://automation.workflow.project.scaffolding.and.template.generation","name":"project scaffolding and template generation","description":"Provides CLI tooling (create-mcp-use-app) for generating boilerplate MCP application projects with pre-configured dependencies, example code, and build setup. Scaffolding supports multiple project types (MCP server, MCP client, MCP agent) and languages (TypeScript, Python), with automatic dependency installation and development environment setup. Generated projects include example implementations and configuration templates.","intents":["Quickly bootstrap a new MCP server project without manual setup","Generate starter code for MCP agents or clients with working examples","Set up development environment with correct dependencies and build tools"],"best_for":["New developers getting started with MCP-use framework","Teams standardizing on MCP project structure and conventions","Rapid prototyping of MCP applications with minimal setup overhead"],"limitations":["Generated projects assume standard directory structure; customization requires manual refactoring","Templates are fixed at release time; no runtime template customization or plugin system","Does not generate CI/CD configuration or deployment manifests"],"requires":["Node.js 18+ with npm or pnpm","create-mcp-use-app package installed globally or via npx","Disk space for project directory and node_modules"],"input_types":["project name (string)","project type (server, client, agent)","language preference (TypeScript, Python)"],"output_types":["project directory with source code","package.json/pyproject.toml with dependencies","example implementations and README"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mcp-use-mcp-use__cap_6","uri":"capability://automation.workflow.build.cli.for.mcp.server.compilation.and.bundling","name":"build cli for mcp server compilation and bundling","description":"Provides a CLI tool (mcp-use build) for compiling TypeScript MCP servers into distributable bundles with dependency bundling and optimization. The build tool handles TypeScript compilation, tree-shaking unused code, bundling dependencies, and generating executable entry points. Supports both stdio and HTTP server output formats with automatic platform-specific binary generation.","intents":["Compile a TypeScript MCP server into a standalone executable for distribution","Bundle MCP server with all dependencies into a single deployable artifact","Generate platform-specific binaries (macOS, Linux, Windows) from TypeScript source"],"best_for":["MCP server developers preparing applications for production deployment","Teams distributing MCP servers as standalone tools or npm packages","Organizations deploying MCP servers to edge functions or serverless environments"],"limitations":["TypeScript-only; does not support Python server compilation","Binary generation requires platform-specific build environment or cross-compilation setup","No built-in code signing or notarization for macOS/Windows distribution"],"requires":["Node.js 18+ with TypeScript 5.0+","mcp-use CLI package installed globally or via npx","TypeScript source files with valid MCP server implementation"],"input_types":["TypeScript source directory","build configuration (entry point, output format, target platform)"],"output_types":["compiled JavaScript bundle","platform-specific executable binary","source maps for debugging"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mcp-use-mcp-use__cap_7","uri":"capability://data.processing.analysis.streaming.and.structured.output.handling","name":"streaming and structured output handling","description":"Enables agents and clients to handle streaming responses from tools and LLMs, with support for structured output parsing and validation. The framework provides streaming abstractions that yield partial results as they arrive, with optional JSON schema validation for structured responses. Streaming works across both Python and TypeScript implementations with consistent API surface.","intents":["Stream long-running tool results to users in real-time instead of waiting for completion","Parse and validate structured JSON output from tools against predefined schemas","Build responsive UIs that display partial results as they become available"],"best_for":["Developers building real-time AI applications with streaming UI updates","Teams requiring structured output validation for tool results","Applications with long-running tools that benefit from progressive result delivery"],"limitations":["Streaming requires explicit handler implementation; not automatically inferred from tool definitions","Schema validation adds latency proportional to schema complexity; no lazy validation option","Streaming state is not persisted; connection loss requires manual retry logic"],"requires":["Python 3.8+ or Node.js 18+","Tool implementations that support streaming (yield/async generator)","JSON schema definitions for structured output validation (optional)"],"input_types":["streaming tool results (iterables or async iterables)","JSON schema for output validation","partial result handlers (callbacks)"],"output_types":["streamed partial results","validated structured objects","completion signals"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mcp-use-mcp-use__cap_8","uri":"capability://memory.knowledge.memory.and.conversation.context.management","name":"memory and conversation context management","description":"Manages conversation history and context across multiple agent interactions, with support for different memory strategies (sliding window, summarization, full history). The framework maintains context state, handles token counting for context windows, and provides hooks for custom memory implementations. Memory is integrated into agent reasoning loop to inform tool selection and response generation.","intents":["Maintain conversation history across multiple agent interactions","Automatically manage context window size to fit within LLM token limits","Implement custom memory strategies (e.g., semantic summarization, hierarchical context)"],"best_for":["Developers building multi-turn conversational agents","Teams implementing long-running agent sessions with memory requirements","Applications requiring custom context management strategies"],"limitations":["Memory is in-memory only; no built-in persistence to database or file storage","Token counting requires LLM-specific tokenizer; not automatically inferred","Context summarization requires external LLM calls, adding latency and cost"],"requires":["Python 3.8+ or Node.js 18+","LLM tokenizer library (e.g., tiktoken for OpenAI models)","Optional: external storage backend for persistence"],"input_types":["conversation messages (user, assistant, tool results)","memory strategy configuration","context window size limits"],"output_types":["conversation history with metadata","truncated context for LLM input","memory statistics (token count, message count)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mcp-use-mcp-use__cap_9","uri":"capability://automation.workflow.middleware.pipeline.for.observability.and.custom.logic.injection","name":"middleware pipeline for observability and custom logic injection","description":"Implements a middleware pipeline architecture that allows injecting custom logic at key points in agent execution: before/after tool invocation, before/after LLM calls, and on errors. Middleware receives execution context (tool name, parameters, results) and can modify behavior, log telemetry, or trigger side effects. Pipeline is composable, allowing multiple middleware to chain together.","intents":["Add logging and telemetry to agent execution without modifying core agent code","Implement custom validation or transformation of tool parameters and results","Build observability layers that track agent behavior and performance metrics"],"best_for":["Teams building production agent systems requiring observability","Developers implementing custom agent behavior through middleware composition","Organizations needing audit trails and compliance logging for agent actions"],"limitations":["Middleware execution adds latency proportional to number of middleware in pipeline","Error handling in middleware can mask underlying tool failures if not carefully implemented","Middleware state is not isolated; shared state across middleware can cause subtle bugs"],"requires":["Python 3.8+ or Node.js 18+","Understanding of middleware pattern and async/await execution model","Telemetry backend (optional) for storing observability data"],"input_types":["middleware functions (async callables)","execution context (tool name, parameters, results)","configuration options"],"output_types":["modified execution context","telemetry events","side effects (logging, metrics, external API calls)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+ (Python SDK) or Node.js 18+ (TypeScript SDK)","At least one MCP server running and accessible via stdio or HTTP transport","LLM API key (OpenAI, Anthropic, or compatible provider) for agent reasoning","Python 3.8+ with mcp-use package or Node.js 18+ with @mcp-use/client","Running MCP server accessible via stdio, HTTP, or SSE transport","Knowledge of target tool names and parameter schemas","Python 3.8+ or Node.js 18+","Configuration file in YAML or JSON format","Environment variables or secret store for sensitive credentials","Linux kernel with container support (cgroups, namespaces) or Docker/Podman installed"],"failure_modes":["Streaming and structured output require explicit configuration; default behavior may not preserve all response metadata","Multi-server coordination adds latency proportional to number of concurrent tool calls","Python and TypeScript implementations have feature parity gaps in advanced observability features","No built-in retry logic or circuit breaker; requires external orchestration for fault tolerance","Synchronous Python client blocks on I/O; async client requires asyncio event loop management","Tool schema validation happens at call time, not parse time; invalid parameters fail at runtime","Configuration schema is fixed; complex deployment scenarios may require code customization","No built-in secret management; requires external secret store integration","Configuration validation happens at runtime; invalid configs fail during initialization","Sandboxing adds significant overhead (container startup, resource limits enforcement); not suitable for latency-sensitive applications","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.667647291147931,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:31.492Z","last_commit":"2026-05-01T17:08:02Z"},"community":{"stars":9878,"forks":1266,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-mcp-use-mcp-use","compare_url":"https://unfragile.ai/compare?artifact=mcp-mcp-use-mcp-use"}},"signature":"trqY9CofW6DsnqnJiuwgQC1Op6MWRLNrYUfg8mC/he9Ly0nB6LzRvAuq20OlKhWdWbAE7KiNIzAnzuaShX3ICw==","signedAt":"2026-06-19T21:52:18.309Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-mcp-use-mcp-use","artifact":"https://unfragile.ai/mcp-mcp-use-mcp-use","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-mcp-use-mcp-use","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"}}