{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-mcp-use--mcp-use","slug":"mcp-use--mcp-use","name":"mcp-use","type":"mcp","url":"https://manufact.com","page_url":"https://unfragile.ai/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-use--mcp-use__cap_0","uri":"capability://planning.reasoning.multi.language.mcp.agent.orchestration.with.tool.aware.reasoning","name":"multi-language mcp agent orchestration with tool-aware reasoning","description":"Implements MCPAgent classes in both Python and TypeScript that enable LLMs to reason across multiple steps using MCP-exposed tools, managing tool discovery, invocation, and result integration into agent context. Uses a middleware pipeline architecture to intercept and transform tool calls, supporting streaming responses and structured output formats while maintaining conversation state across multi-turn interactions.","intents":["Build an AI agent that can autonomously decide which tools to call and in what sequence to solve a problem","Create agents that maintain conversation history and tool execution context across multiple reasoning steps","Integrate custom tools into an agent without modifying core agent logic"],"best_for":["Teams building autonomous AI agents that need to orchestrate multiple external tools","Developers migrating from single-tool integrations to multi-step agentic workflows","Organizations needing parallel Python/TypeScript implementations of the same agent logic"],"limitations":["Middleware pipeline adds ~50-100ms latency per tool invocation due to serialization/deserialization overhead","Streaming responses require explicit configuration per agent instance; not enabled by default","Conversation memory is in-process only — requires external persistence layer (Redis, database) for production deployments","TypeScript and Python implementations have feature parity gaps; some advanced features (e.g., custom middleware) are TS-only"],"requires":["Python 3.9+ (for Python SDK) or Node.js 18+ (for 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 (strings)","structured tool schemas (JSON)","conversation history (message arrays)"],"output_types":["tool invocation requests (structured JSON)","agent reasoning traces (text with metadata)","streaming token sequences (for real-time UI updates)"],"categories":["planning-reasoning","tool-use-integration","agentic-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mcp-use--mcp-use__cap_1","uri":"capability://tool.use.integration.mcp.client.library.for.programmatic.tool.invocation.without.llm","name":"mcp client library for programmatic tool invocation without llm","description":"Provides MCPClient classes (Python and TypeScript) that establish connections to MCP servers and enable direct, synchronous invocation of exposed tools without requiring an LLM in the loop. Handles transport protocol abstraction (stdio, HTTP, WebSocket), server capability discovery, and result marshaling into native language types, allowing developers to use MCP tools as a standard library.","intents":["Call MCP tools directly from application code without spinning up an LLM agent","Build CLI tools or scripts that leverage MCP server capabilities","Integrate MCP tools into existing non-agentic applications (e.g., data pipelines, batch jobs)"],"best_for":["Backend engineers building data pipelines that need to call external tools","DevOps teams automating infrastructure tasks via MCP servers","Developers prototyping tool integrations before building full agents"],"limitations":["No built-in retry logic or circuit breaker — requires wrapping client calls in application-level error handling","Synchronous API only; async/await patterns not supported in Python SDK (TypeScript SDK has async support)","No connection pooling — each MCPClient instance opens a new server connection, leading to resource overhead at scale","Transport protocol selection is static per client instance; cannot switch protocols at runtime"],"requires":["Python 3.9+ or Node.js 18+","Running MCP server accessible via stdio, HTTP, or WebSocket","Knowledge of MCP server's tool schemas (obtained via server introspection)"],"input_types":["tool names (strings)","tool arguments (JSON-serializable objects)","transport configuration (host, port, command)"],"output_types":["tool results (native Python/TypeScript objects)","error responses (structured exceptions)","server metadata (capability lists, schema definitions)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mcp-use--mcp-use__cap_10","uri":"capability://automation.workflow.observability.and.telemetry.collection.for.agent.execution","name":"observability and telemetry collection for agent execution","description":"Provides built-in telemetry collection that tracks agent execution metrics (tool invocation counts, latency, error rates), reasoning traces (step-by-step agent decisions), and resource usage (token counts, memory). Integrates with standard observability platforms (OpenTelemetry, Datadog, CloudWatch) for centralized monitoring and alerting.","intents":["Monitor agent performance in production and identify bottlenecks","Debug agent failures by analyzing execution traces and error logs","Track resource usage (tokens, API calls) for cost optimization"],"best_for":["Teams running agents in production requiring operational visibility","Organizations needing cost tracking for LLM API usage","DevOps teams building agent monitoring dashboards"],"limitations":["Telemetry collection adds ~5-10% overhead to agent execution time","OpenTelemetry integration requires external collector/backend; no built-in storage","Trace sampling must be configured manually; high-volume agents can generate excessive telemetry","No built-in alerting; requires external monitoring platform (Datadog, New Relic, etc.)"],"requires":["OpenTelemetry SDK (optional, for advanced telemetry)","External observability backend (Datadog, CloudWatch, Prometheus, etc.) for centralized collection","Configuration of telemetry exporters in agent setup"],"input_types":["agent execution events (tool invocations, reasoning steps, errors)"],"output_types":["metrics (counters, histograms, gauges)","traces (execution traces with timing and context)","logs (structured logs with context)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mcp-use--mcp-use__cap_11","uri":"capability://safety.moderation.sandboxed.execution.environment.for.untrusted.tool.code","name":"sandboxed execution environment for untrusted tool code","description":"Provides optional sandboxing for tool execution that isolates untrusted code from the host system, preventing malicious tools from accessing files, network, or system resources. Uses OS-level isolation (containers, VMs) or JavaScript sandboxing (for TypeScript tools) to enforce resource limits and capability restrictions.","intents":["Safely execute tools from untrusted sources without compromising host security","Limit resource usage (CPU, memory, disk) of individual tools to prevent DoS","Audit and control what system resources tools can access"],"best_for":["Organizations running user-provided tools or third-party integrations","Multi-tenant SaaS platforms where tool isolation is critical","Teams with strict security requirements for tool execution"],"limitations":["Sandboxing adds significant overhead (100-500ms per invocation for container-based sandboxing)","Container-based sandboxing requires Docker or similar container runtime","JavaScript sandboxing (for TypeScript) is less secure than OS-level isolation; can be escaped with effort","Sandboxed tools cannot access host network or filesystem; requires explicit allowlisting of resources"],"requires":["Docker or container runtime (for container-based sandboxing)","Explicit sandbox configuration per tool (resource limits, allowed capabilities)","Security policy definition (what resources tools can access)"],"input_types":["tool code (JavaScript/TypeScript or compiled binary)","sandbox configuration (resource limits, allowed capabilities)"],"output_types":["tool execution result (with resource usage metrics)","sandbox violation alerts (if tool attempts unauthorized access)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mcp-use--mcp-use__cap_12","uri":"capability://automation.workflow.configuration.management.and.environment.based.deployment","name":"configuration management and environment-based deployment","description":"Implements configuration file formats (YAML, JSON) and environment variable support that allow agents and servers to be configured without code changes, enabling different configurations for development, staging, and production environments. Supports configuration inheritance, variable substitution, and validation against schemas.","intents":["Deploy the same agent code to multiple environments with different configurations","Manage secrets (API keys, credentials) via environment variables instead of hardcoding","Validate configuration at startup to catch errors early"],"best_for":["Teams deploying agents across multiple environments (dev, staging, prod)","Organizations managing secrets and credentials via environment variables","DevOps teams automating agent deployment via infrastructure-as-code"],"limitations":["Configuration validation is schema-based; complex validation logic must be implemented manually","No built-in configuration hot-reloading; configuration changes require agent restart","Environment variable substitution is simple string replacement; no type coercion","Configuration inheritance can be confusing if multiple files override the same settings"],"requires":["Configuration file (YAML or JSON) in expected location","Environment variables for secrets (if using env var substitution)"],"input_types":["configuration files (YAML, JSON)","environment variables"],"output_types":["parsed configuration object (native Python/TypeScript types)","validation errors (if configuration is invalid)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mcp-use--mcp-use__cap_13","uri":"capability://safety.moderation.authentication.and.authorization.for.mcp.server.access","name":"authentication and authorization for mcp server access","description":"Provides authentication mechanisms (API keys, OAuth2, mTLS) for securing MCP server access, ensuring only authorized clients can invoke tools. Supports per-server authentication configuration and integrates with standard auth providers (OpenAI, Anthropic, custom OAuth2 servers).","intents":["Restrict MCP server access to authorized clients only","Implement multi-tenant access control where different users have different tool permissions","Integrate with existing authentication infrastructure (OAuth2, SAML)"],"best_for":["Organizations exposing MCP servers over the internet requiring access control","Multi-tenant SaaS platforms with per-user tool permissions","Teams integrating MCP with existing identity management systems"],"limitations":["Authentication is per-server; no centralized authorization across multiple servers","OAuth2 integration requires external OAuth2 provider; no built-in auth server","mTLS requires certificate management; adds operational complexity","No built-in role-based access control (RBAC); must be implemented in custom middleware"],"requires":["Authentication provider (API key, OAuth2 server, mTLS certificates)","Server configuration with auth credentials"],"input_types":["authentication credentials (API key, OAuth2 token, client certificate)"],"output_types":["authenticated connection (if credentials are valid)","authentication error (if credentials are invalid)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mcp-use--mcp-use__cap_2","uri":"capability://code.generation.editing.mcp.server.scaffolding.and.code.generation.for.typescript","name":"mcp server scaffolding and code generation for typescript","description":"Provides create-mcp-use-app CLI tool and build system that generates boilerplate MCP server projects with pre-configured tool, resource, and prompt handlers. Uses TypeScript decorators and class-based patterns to define server capabilities, automatically generating MCP protocol-compliant schemas and handling transport setup (stdio, HTTP) without manual protocol implementation.","intents":["Quickly scaffold a new MCP server without writing boilerplate transport code","Define tools, resources, and prompts using familiar TypeScript class syntax","Generate MCP-compliant server implementations that work with Claude, ChatGPT, and other MCP clients"],"best_for":["TypeScript developers building custom MCP servers for the first time","Teams standardizing on MCP server patterns across multiple projects","Organizations exposing internal tools via MCP without writing protocol handlers manually"],"limitations":["TypeScript only — no Python server scaffolding (Python developers must write servers manually or use TypeScript bridge)","Decorator-based syntax requires TypeScript 5.0+ with experimentalDecorators enabled","Generated servers require explicit transport configuration; no auto-detection of optimal transport based on environment","Scaffolding generates opinionated project structure; customization requires manual file edits outside generated code"],"requires":["Node.js 18+","TypeScript 5.0+ with decorator support enabled in tsconfig.json","npm or pnpm package manager"],"input_types":["CLI prompts (project name, tool names)","TypeScript class definitions (for custom tools)"],"output_types":["TypeScript project directory with package.json, tsconfig.json, and src/ structure","MCP server implementation with tool/resource/prompt handlers","Build configuration (esbuild or tsc setup)"],"categories":["code-generation-editing","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mcp-use--mcp-use__cap_3","uri":"capability://tool.use.integration.multi.server.management.and.connector.abstraction","name":"multi-server management and connector abstraction","description":"Implements Connectors and Sessions (Python) and multi-server management patterns that allow agents and clients to connect to multiple MCP servers simultaneously, routing tool calls to the correct server based on tool availability. Uses a session-based architecture where each session maintains independent server connections and state, enabling isolation between concurrent agent instances or multi-tenant scenarios.","intents":["Build agents that can call tools from multiple MCP servers in a single reasoning loop","Manage multiple independent agent sessions without cross-contamination of state","Route tool invocations to the correct server when multiple servers expose overlapping tool names"],"best_for":["Teams running multiple specialized MCP servers (e.g., one for file ops, one for API calls, one for data queries)","SaaS platforms supporting multi-tenant agent deployments with isolated server connections per tenant","Organizations with complex tool ecosystems requiring dynamic server discovery and failover"],"limitations":["No built-in load balancing across multiple servers exposing the same tool; first-match routing is used","Session state is not automatically persisted; requires external storage for recovery after crashes","Connector abstraction adds ~30-50ms overhead per tool invocation due to server routing logic","No automatic server health checks or failover — dead servers must be manually removed from connector configuration"],"requires":["Python 3.9+ (for Connectors/Sessions API)","Multiple MCP servers running and accessible via configured transports","Explicit server registration in connector configuration (no auto-discovery)"],"input_types":["server connection configs (host, port, command, transport type)","tool invocation requests (tool name + arguments)"],"output_types":["routed tool results from appropriate server","server availability status (for health checks)","session metadata (active connections, tool inventory)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mcp-use--mcp-use__cap_4","uri":"capability://tool.use.integration.mcp.protocol.schema.introspection.and.capability.discovery","name":"mcp protocol schema introspection and capability discovery","description":"Implements automatic server capability discovery that queries MCP servers for their exposed tools, resources, and prompts, returning structured schema definitions that agents and clients use to understand what operations are available. Uses MCP protocol's list_tools, list_resources, and list_prompts messages to dynamically build capability inventories without requiring hardcoded tool definitions.","intents":["Automatically discover what tools a server exposes without reading documentation","Build dynamic UIs that show available tools based on connected servers","Validate tool invocations against server schemas before sending them"],"best_for":["Developers building generic MCP clients that work with any server","Teams with frequently-changing tool inventories that need dynamic capability awareness","UI builders creating tool selection interfaces for end users"],"limitations":["Schema introspection adds ~100-200ms latency on first connection; results should be cached","No built-in caching of schema results — applications must implement their own cache invalidation strategy","Introspection is read-only; cannot modify server capabilities or schemas from client side","Schema format is MCP protocol-specific; converting to OpenAPI or other formats requires custom mapping"],"requires":["Connected MCP server supporting list_tools, list_resources, list_prompts messages","Network connectivity to server (for HTTP/WebSocket transports)"],"input_types":["server connection (MCPClient or Agent instance)"],"output_types":["tool schemas (name, description, input schema, output schema)","resource schemas (name, URI pattern, MIME type)","prompt schemas (name, description, arguments)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mcp-use--mcp-use__cap_5","uri":"capability://text.generation.language.streaming.and.structured.output.formatting.for.agent.responses","name":"streaming and structured output formatting for agent responses","description":"Provides streaming response handling that allows agents to emit tokens incrementally as they reason, enabling real-time UI updates and progressive result delivery. Supports structured output formats (JSON, XML) that agents can use to return results in machine-readable form, with automatic schema validation and type coercion to native language objects.","intents":["Display agent reasoning in real-time as tokens arrive instead of waiting for full completion","Return agent results in structured formats (JSON) that applications can parse and process programmatically","Build interactive UIs that update as agent thinks, improving perceived responsiveness"],"best_for":["Web applications and chat interfaces requiring real-time agent feedback","Data processing pipelines that need structured agent outputs for downstream processing","Teams building agent monitoring dashboards that visualize reasoning steps"],"limitations":["Streaming requires compatible LLM provider (OpenAI, Anthropic); local models may not support streaming","Structured output validation adds ~20-50ms per response; schema mismatches cause parsing errors","Streaming responses cannot be retried mid-stream; partial results are lost if connection drops","Structured output format must be explicitly configured per agent; no auto-detection of optimal format"],"requires":["LLM provider with streaming API support (OpenAI with gpt-4-turbo, Anthropic with Claude 3+)","WebSocket or HTTP long-polling for client-side streaming reception","JSON-Schema definitions for structured output validation"],"input_types":["agent configuration (streaming enabled/disabled)","output schema (JSON-Schema for structured outputs)"],"output_types":["token streams (for real-time display)","structured objects (JSON parsed to native types)","streaming metadata (token counts, timing)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mcp-use--mcp-use__cap_6","uri":"capability://memory.knowledge.memory.and.conversation.state.management.across.agent.turns","name":"memory and conversation state management across agent turns","description":"Implements conversation history tracking and memory management that persists agent reasoning steps, tool invocations, and results across multiple turns, enabling agents to reference prior context when making decisions. Uses message-based architecture where each turn appends to a conversation log, with configurable memory strategies (full history, sliding window, summarization) to manage context window constraints.","intents":["Build multi-turn agents that remember what happened in previous interactions","Implement conversation summarization to fit long histories into LLM context windows","Debug agent behavior by replaying conversation history"],"best_for":["Chatbot and assistant applications requiring persistent conversation context","Teams building agents that need to learn from past interactions within a session","Developers debugging agent behavior by analyzing full conversation traces"],"limitations":["In-process memory only — conversation history is lost when agent process terminates; requires external persistence","No built-in compression or summarization; full conversation history grows unbounded until manually pruned","Memory strategies (sliding window, summarization) must be configured manually; no automatic optimization","Conversation state is not automatically synchronized across multiple agent instances; requires external coordination"],"requires":["Agent instance with memory enabled in configuration","Optional: external storage (database, Redis) for persistence across process restarts"],"input_types":["user messages (strings)","tool invocation results (structured objects)"],"output_types":["conversation history (array of message objects with role, content, metadata)","memory statistics (turn count, token count, memory strategy)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mcp-use--mcp-use__cap_7","uri":"capability://tool.use.integration.mcp.inspector.interactive.debugging.and.protocol.visualization","name":"mcp inspector interactive debugging and protocol visualization","description":"Provides a web-based interactive debugger (MCP Inspector) that visualizes MCP protocol messages in real-time, allowing developers to inspect tool schemas, test tool invocations, and debug server/client communication. Displays request/response pairs with syntax highlighting, enables manual tool invocation with JSON argument editing, and logs all protocol messages for post-mortem analysis.","intents":["Debug MCP server implementations by inspecting protocol messages","Test MCP tools interactively without writing client code","Understand what messages are being exchanged between client and server"],"best_for":["MCP server developers debugging tool implementations","Teams integrating new MCP servers and validating protocol compliance","DevOps engineers troubleshooting server connectivity issues"],"limitations":["Inspector runs as a separate web service; requires additional port and process management","No built-in authentication — should not be exposed to untrusted networks","Message logging is in-memory only; logs are lost when inspector process restarts","Inspector cannot modify server state or tool definitions; read-only debugging only"],"requires":["Node.js 18+ (for running inspector)","Running MCP server accessible via stdio or HTTP","Web browser for accessing inspector UI (Chrome, Firefox, Safari)"],"input_types":["MCP server connection (stdio command or HTTP URL)","manual tool invocation arguments (JSON)"],"output_types":["protocol message visualization (request/response pairs)","tool schema documentation (formatted JSON-Schema)","execution logs (with timestamps and message types)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mcp-use--mcp-use__cap_8","uri":"capability://tool.use.integration.transport.protocol.abstraction.and.negotiation.stdio.http.websocket","name":"transport protocol abstraction and negotiation (stdio, http, websocket)","description":"Abstracts MCP transport mechanisms (stdio, HTTP, WebSocket) behind a unified interface, allowing servers and clients to be deployed with different transports without code changes. Handles protocol negotiation, connection lifecycle management, and message serialization/deserialization for each transport type, with automatic fallback and error handling.","intents":["Deploy MCP servers via stdio for local development and HTTP for cloud deployments without code changes","Connect to MCP servers using the most appropriate transport for the environment (stdio for same-machine, HTTP for remote)","Build MCP infrastructure that works across diverse deployment scenarios"],"best_for":["Teams deploying MCP servers across multiple environments (local, cloud, edge)","Organizations standardizing on MCP but needing flexibility in deployment topology","Developers building MCP infrastructure tools that must work with various server configurations"],"limitations":["Transport selection is static per connection; cannot switch transports at runtime without reconnecting","HTTP transport requires explicit server URL configuration; no auto-discovery of HTTP servers","WebSocket transport requires additional infrastructure (proxy, load balancer) for production deployments","Stdio transport is limited to local connections; cannot be used for remote servers"],"requires":["Python 3.9+ or Node.js 18+","For stdio: MCP server command accessible in PATH or as absolute path","For HTTP: MCP server running on accessible host:port with CORS configured (if cross-origin)","For WebSocket: MCP server with WebSocket support and network connectivity"],"input_types":["transport type (stdio, http, websocket)","transport configuration (command, URL, headers)"],"output_types":["transport connection (abstracted as unified client interface)","protocol messages (JSON-RPC format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mcp-use--mcp-use__cap_9","uri":"capability://tool.use.integration.middleware.pipeline.for.tool.invocation.interception.and.transformation","name":"middleware pipeline for tool invocation interception and transformation","description":"Implements a middleware pipeline architecture that allows custom interceptors to be inserted into the tool invocation flow, enabling cross-cutting concerns like logging, caching, validation, and result transformation without modifying agent or tool code. Middleware runs in sequence, with each middleware able to inspect/modify requests before invocation and responses after completion.","intents":["Add logging and monitoring to all tool invocations without changing agent code","Implement caching for expensive tool calls to reduce latency","Validate tool arguments against custom rules before sending to server","Transform tool results into different formats for downstream processing"],"best_for":["Teams needing observability across tool invocations without instrumentation code","Organizations implementing caching strategies for expensive external tools","Developers building extensible agent frameworks with pluggable behavior"],"limitations":["Middleware pipeline adds ~10-30ms overhead per tool invocation due to sequential execution","Middleware order matters; incorrect ordering can cause unexpected behavior (e.g., caching before validation)","No built-in middleware for common patterns (logging, caching); must be implemented manually or via third-party packages","Middleware exceptions can break the pipeline; requires careful error handling in each middleware"],"requires":["TypeScript SDK (Python SDK has limited middleware support)","Understanding of middleware pattern and async/await for custom middleware"],"input_types":["tool invocation request (tool name, arguments)","tool response (result or error)"],"output_types":["modified request (if middleware transforms arguments)","modified response (if middleware transforms results)","side effects (logging, caching, metrics)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+ (for Python SDK) or Node.js 18+ (for 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 3.9+ or Node.js 18+","Running MCP server accessible via stdio, HTTP, or WebSocket","Knowledge of MCP server's tool schemas (obtained via server introspection)","OpenTelemetry SDK (optional, for advanced telemetry)","External observability backend (Datadog, CloudWatch, Prometheus, etc.) for centralized collection","Configuration of telemetry exporters in agent setup","Docker or container runtime (for container-based sandboxing)"],"failure_modes":["Middleware pipeline adds ~50-100ms latency per tool invocation due to serialization/deserialization overhead","Streaming responses require explicit configuration per agent instance; not enabled by default","Conversation memory is in-process only — requires external persistence layer (Redis, database) for production deployments","TypeScript and Python implementations have feature parity gaps; some advanced features (e.g., custom middleware) are TS-only","No built-in retry logic or circuit breaker — requires wrapping client calls in application-level error handling","Synchronous API only; async/await patterns not supported in Python SDK (TypeScript SDK has async support)","No connection pooling — each MCPClient instance opens a new server connection, leading to resource overhead at scale","Transport protocol selection is static per client instance; cannot switch protocols at runtime","Telemetry collection adds ~5-10% overhead to agent execution time","OpenTelemetry integration requires external collector/backend; no built-in storage","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6676401352713437,"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":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:56:56.344Z","last_commit":"2026-05-01T17:08:02Z"},"community":{"stars":9877,"forks":1266,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-use--mcp-use","compare_url":"https://unfragile.ai/compare?artifact=mcp-use--mcp-use"}},"signature":"vYq8wCp/ZbJvTdLtluwCX4cyWrMnPB1TQay7JuEGIzTgF0egWtOvqskzJ5bH8JIoy9WKlAgM24MCLQxVbayVBQ==","signedAt":"2026-06-21T18:49:21.713Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-use--mcp-use","artifact":"https://unfragile.ai/mcp-use--mcp-use","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}