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The MCPApp class in arcade_mcp_server/mcp_app.py introspects function signatures, auto-generates JSON schemas from type hints, and registers tools into a ToolCatalog for MCP protocol exposure. Supports async functions, dependency injection via context parameters, and automatic schema validation.","intents":["I want to expose a Python function as an MCP tool with minimal code","I need automatic JSON schema generation from my function signatures","I want to use familiar FastAPI-style decorators for tool definition"],"best_for":["Python developers familiar with FastAPI patterns","Teams building MCP servers without extensive boilerplate","Rapid prototyping of tool-exposing services"],"limitations":["Python-only implementation; no native support for other languages","Schema generation relies on type hints; untyped functions produce generic schemas","Decorator-based registration happens at import time, limiting dynamic tool loading"],"requires":["Python 3.9+","arcade-mcp-server package installed","Type hints on function parameters for optimal schema generation"],"input_types":["Python function with typed parameters","Async or sync callables"],"output_types":["MCP ToolDefinition schema","Registered tool in ToolCatalog"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-arcadeai-arcade-mcp__cap_1","uri":"capability://tool.use.integration.multi.transport.mcp.protocol.bridging.stdio.and.http.sse","name":"multi-transport mcp protocol bridging (stdio and http/sse)","description":"Implements dual transport layer supporting both stdio (for desktop clients like Claude Desktop, Cursor) and HTTP with Server-Sent Events (for web-based clients). The StdioTransport and HTTPSessionManager classes handle protocol framing, message serialization, and bidirectional communication. Allows single MCP server to serve both local IDE integrations and remote web clients without code changes.","intents":["I want my MCP server to work with Claude Desktop and Cursor IDE simultaneously","I need to expose MCP tools to web-based clients over HTTP","I want to support both local and remote client connections from one server"],"best_for":["Teams deploying MCP servers for both desktop and web environments","Developers building hybrid local/cloud tool ecosystems","Organizations needing IDE integration plus API access"],"limitations":["HTTP transport requires explicit SSE configuration; standard REST clients cannot consume MCP protocol directly","Stdio transport is blocking; high-concurrency scenarios may require process pooling","Transport selection is compile-time; cannot dynamically switch transports per connection"],"requires":["Python 3.9+","arcade-mcp-server with transport modules","For HTTP: web server framework (FastAPI/Starlette)","For stdio: compatible MCP client (Claude Desktop 0.1+, Cursor 0.42+)"],"input_types":["MCP protocol messages (JSON-RPC 2.0)","Tool invocation requests"],"output_types":["MCP protocol responses","Tool execution results","Server-Sent Events stream (HTTP)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-arcadeai-arcade-mcp__cap_10","uri":"capability://data.processing.analysis.usage.tracking.and.analytics","name":"usage tracking and analytics","description":"Provides built-in usage tracking capturing tool invocations, execution time, errors, and resource consumption. Metrics are collected automatically via middleware and can be exported to monitoring systems (Prometheus, CloudWatch, etc.). Supports custom metrics and event tagging for detailed analysis. Data is aggregated per tool, user, and session.","intents":["I want to track which tools are used most frequently","I need to monitor tool performance and error rates","I want to analyze usage patterns by user or session"],"best_for":["Production MCP servers requiring observability","Teams optimizing tool usage and performance","Organizations tracking tool adoption and ROI"],"limitations":["Metrics are in-memory; no persistence across restarts without export","Export to external systems requires custom integration","Real-time alerting is not built-in; requires external monitoring"],"requires":["Python 3.9+","arcade-mcp-server with usage tracking","Optional: monitoring system (Prometheus, CloudWatch, etc.)"],"input_types":["Tool invocation (automatic capture)"],"output_types":["Usage metrics (invocation count, latency, errors)","Exported metrics (Prometheus format, CloudWatch, etc.)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-arcadeai-arcade-mcp__cap_11","uri":"capability://memory.knowledge.resources.and.prompts.system","name":"resources and prompts system","description":"Implements MCP resources and prompts as first-class abstractions. Resources are static or dynamic data (files, API responses, database records) exposed via MCP. Prompts are reusable instruction templates with parameters. Framework provides decorators (@app.resource, @app.prompt) for registration and automatic schema generation. Clients can discover and invoke resources/prompts alongside tools.","intents":["I want to expose files or data as MCP resources","I need to provide reusable prompt templates to clients","I want clients to discover available resources and prompts"],"best_for":["MCP servers exposing data alongside tools","Teams building prompt libraries for AI assistants","Applications requiring dynamic resource discovery"],"limitations":["Resources are read-only in current implementation; no write support","Prompt parameters are simple (strings); no complex type support","Resource discovery is flat; no hierarchical organization"],"requires":["Python 3.9+","arcade-mcp-server with resources/prompts support"],"input_types":["Resource URI or prompt name with parameters"],"output_types":["Resource data (file content, API response, etc.)","Rendered prompt with parameters substituted"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-arcadeai-arcade-mcp__cap_12","uri":"capability://safety.moderation.error.handling.and.exception.propagation","name":"error handling and exception propagation","description":"Provides structured error handling with custom exception types (ToolExecutionError, AuthenticationError, ValidationError) that are automatically serialized to MCP error responses. Tools can raise exceptions with user-friendly messages and error codes; framework catches and formats for client consumption. Supports error context (stack traces, debugging info) in development mode.","intents":["I want tool errors to be clearly communicated to clients","I need to distinguish between different error types (auth, validation, execution)","I want detailed error context in development but sanitized messages in production"],"best_for":["Production MCP servers requiring robust error handling","Teams debugging tool failures","Applications needing user-friendly error messages"],"limitations":["Error context (stack traces) is only available in development mode; production errors are sanitized","Custom exception types require subclassing; limited pre-built error patterns","Error recovery is manual; no built-in retry logic"],"requires":["Python 3.9+","arcade-mcp-server with error handling"],"input_types":["Exception raised by tool"],"output_types":["MCP error response with code and message","Detailed error context (development mode)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-arcadeai-arcade-mcp__cap_13","uri":"capability://automation.workflow.configuration.management.and.environment.based.settings","name":"configuration management and environment-based settings","description":"Implements MCPSettings class (arcade_mcp_server/settings.py) using Pydantic for configuration management. Settings are loaded from environment variables, .env files, or config files with type validation and defaults. Supports environment-specific overrides (dev, staging, prod) and secrets resolution. Configuration is immutable after initialization, preventing runtime changes.","intents":["I want to configure my MCP server via environment variables","I need different settings for dev/staging/production","I want type-safe configuration with validation"],"best_for":["Production MCP servers requiring environment-specific config","Teams managing multiple deployment environments","Applications needing secure credential management"],"limitations":["Configuration is immutable after initialization; no runtime updates","Pydantic validation adds startup overhead (~100ms for complex configs)","Environment variable naming is manual; no automatic snake_case conversion"],"requires":["Python 3.9+","Pydantic 2.0+","Environment variables or .env file"],"input_types":["Environment variables",".env file","Config file (YAML, TOML)"],"output_types":["Validated MCPSettings object"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-arcadeai-arcade-mcp__cap_14","uri":"capability://automation.workflow.docker.and.cloud.deployment.packaging","name":"docker and cloud deployment packaging","description":"Provides Docker support via Dockerfile templates and cloud deployment via 'arcade deploy' command. Framework generates optimized Docker images with minimal layers, caches dependencies, and supports multi-stage builds. Deployment to Arcade Cloud is one-command (arcade deploy) with automatic scaling, monitoring, and HTTPS. Supports environment variable injection and secrets management in cloud.","intents":["I want to containerize my MCP server for deployment","I need to deploy my MCP server to production without managing infrastructure","I want automatic scaling and monitoring for my MCP server"],"best_for":["Teams deploying MCP servers to production","Organizations avoiding Kubernetes complexity","Developers needing quick deployment without DevOps expertise"],"limitations":["Docker support is template-based; custom Dockerfile modifications require manual editing","Arcade Cloud deployment is proprietary; no multi-cloud or self-hosted option","Scaling configuration is automatic; limited customization for specific workloads"],"requires":["Python 3.9+","Docker installed (for local containerization)","Arcade Cloud account (for cloud deployment)","Git repository (for deployment tracking)"],"input_types":["MCP server code","Configuration and environment variables"],"output_types":["Docker image","Deployed server URL on Arcade Cloud"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-arcadeai-arcade-mcp__cap_2","uri":"capability://tool.use.integration.toolkit.ecosystem.with.35.pre.built.integrations","name":"toolkit ecosystem with 35+ pre-built integrations","description":"Provides a modular toolkit system where pre-built tool collections (e.g., GitHub, Slack, Google Workspace, Stripe) are packaged as importable Python modules. Each toolkit registers its tools via the ToolCatalog, with built-in authentication handlers (OAuth2, API keys) and secrets management. Developers import toolkits and optionally customize or extend them without reimplementing integrations.","intents":["I want to quickly add GitHub/Slack/Google Workspace tools to my MCP server","I need pre-built authentication flows for third-party APIs","I want to reuse vetted tool implementations across multiple projects"],"best_for":["Teams building agent applications requiring external service integrations","Developers avoiding custom API integration code","Organizations standardizing on common tool sets across projects"],"limitations":["Toolkit coverage is curated; not all APIs have pre-built toolkits","Customizing toolkit behavior requires subclassing or forking; limited composition patterns","Toolkit versions may lag behind upstream API changes; maintenance dependency on Arcade team"],"requires":["Python 3.9+","Specific toolkit package (e.g., arcade-toolkit-github)","API credentials for target service (GitHub token, Slack bot token, etc.)","Network access to third-party services"],"input_types":["Toolkit import statement","Service credentials (API keys, OAuth tokens)"],"output_types":["Registered tools in ToolCatalog","Tool execution results from external services"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-arcadeai-arcade-mcp__cap_3","uri":"capability://safety.moderation.secrets.management.and.authentication.provider.abstraction","name":"secrets management and authentication provider abstraction","description":"Implements a pluggable authentication layer supporting OAuth2, API keys, and custom auth schemes. The ToolAuthorization and authentication provider classes abstract credential handling, allowing tools to request secrets at runtime via context injection. Secrets are resolved from environment variables, secure vaults, or Arcade Cloud, with automatic token refresh for OAuth2 flows.","intents":["I want tools to securely access API credentials without hardcoding them","I need OAuth2 token refresh without manual intervention","I want to support multiple authentication schemes (API key, OAuth, custom)"],"best_for":["Teams deploying MCP servers in production with sensitive credentials","Applications requiring multi-user OAuth2 flows","Organizations with centralized secret management (Vault, AWS Secrets Manager)"],"limitations":["Local development requires environment variable setup; no built-in secret injection UI","OAuth2 refresh token rotation is automatic but requires persistent storage (not provided)","Custom auth schemes require implementing ToolAuthorization interface; limited pre-built patterns"],"requires":["Python 3.9+","arcade-mcp-server with auth modules","For OAuth2: client ID/secret from service provider","For vault integration: connection details to secret store"],"input_types":["Authentication provider configuration","User credentials or OAuth tokens"],"output_types":["Resolved credentials injected into tool context","Refreshed OAuth2 tokens"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-arcadeai-arcade-mcp__cap_4","uri":"capability://automation.workflow.cli.driven.project.scaffolding.and.deployment","name":"cli-driven project scaffolding and deployment","description":"The arcade CLI (arcade new, arcade mcp, arcade deploy) provides project generation, local server execution, and cloud deployment. 'arcade new' creates a minimal MCP server template with pyproject.toml, tool stubs, and configuration. 'arcade mcp' runs the server locally with hot-reload. 'arcade deploy' packages and deploys to Arcade Cloud with automatic scaling and monitoring. CLI integrates with arcade login for authentication.","intents":["I want to bootstrap a new MCP server project in seconds","I need to run my MCP server locally for testing","I want to deploy my MCP server to production without managing infrastructure"],"best_for":["Solo developers and small teams building MCP servers","Non-technical founders prototyping tool-based agents","Teams avoiding Kubernetes/Docker complexity for MCP deployment"],"limitations":["Project templates are opinionated (Python-only, specific structure); limited customization","Local execution (arcade mcp) lacks advanced debugging features (breakpoints, profiling)","Arcade Cloud deployment is proprietary; no multi-cloud or self-hosted deployment option"],"requires":["Python 3.9+","arcade-cli package installed","For deployment: Arcade Cloud account and API key (arcade login)"],"input_types":["Project name and toolkit selections","Local Python code and configuration"],"output_types":["Scaffolded project directory","Running MCP server (stdio or HTTP)","Deployed server URL on Arcade Cloud"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-arcadeai-arcade-mcp__cap_5","uri":"capability://memory.knowledge.tool.catalog.with.discovery.and.schema.validation","name":"tool catalog with discovery and schema validation","description":"The ToolCatalog class (arcade_core/catalog.py) maintains a registry of all available tools with their JSON schemas, input/output types, and metadata. Tools are registered via decorators or programmatically; catalog provides discovery APIs (list_tools, get_tool) and validates tool invocations against schemas. Supports tool versioning, deprecation markers, and categorization for organization.","intents":["I want to discover all available tools in my MCP server","I need to validate tool inputs before execution","I want to organize tools by category or domain"],"best_for":["MCP servers with 10+ tools requiring organization","Teams building tool discovery UIs or documentation","Applications needing runtime tool validation"],"limitations":["Catalog is in-memory; no persistence across server restarts","Schema validation is JSON-Schema only; no custom validation logic","Tool discovery is flat; no hierarchical organization (only tags/categories)"],"requires":["Python 3.9+","arcade-core package","Tools registered via @app.tool or programmatic API"],"input_types":["Tool definitions with schemas","Tool invocation payloads"],"output_types":["Tool list with metadata","Validation results (pass/fail with error details)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-arcadeai-arcade-mcp__cap_6","uri":"capability://tool.use.integration.ide.client.integration.claude.desktop.cursor.vs.code","name":"ide client integration (claude desktop, cursor, vs code)","description":"Provides configuration and integration patterns for connecting MCP servers to desktop IDEs. Claude Desktop integration uses stdio transport and config file (claude_desktop_config.json). Cursor and VS Code integrations use similar patterns with IDE-specific configuration. Framework handles transport setup; developers configure client connection details (server path, arguments) and Arcade handles protocol negotiation.","intents":["I want my MCP server to appear as a tool provider in Claude Desktop","I need to configure my MCP server for Cursor IDE integration","I want to expose tools to VS Code extensions via MCP"],"best_for":["Developers building tools for AI-assisted coding workflows","Teams integrating custom tools into Claude Desktop","Organizations standardizing on IDE-based tool access"],"limitations":["Configuration is manual (JSON files); no UI-driven setup wizard","IDE integration requires specific IDE versions (Claude Desktop 0.1+, Cursor 0.42+)","Tool availability in IDE depends on IDE's MCP client implementation; not all MCP features exposed"],"requires":["IDE installed (Claude Desktop, Cursor, or VS Code with MCP extension)","MCP server running locally or remotely accessible","Configuration file (claude_desktop_config.json or equivalent) with server details"],"input_types":["Server executable path or HTTP URL","Server arguments and environment variables"],"output_types":["Tools available in IDE's tool palette","Tool execution results in IDE context"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-arcadeai-arcade-mcp__cap_7","uri":"capability://automation.workflow.middleware.and.request.processing.pipeline","name":"middleware and request processing pipeline","description":"Implements a middleware system (similar to FastAPI/Express) allowing request/response interception and transformation. Middleware can log tool invocations, enforce rate limits, transform inputs/outputs, or inject context. Middleware is registered via decorator or programmatically and executes in order before/after tool execution. Supports async middleware for non-blocking operations.","intents":["I want to log all tool invocations for audit purposes","I need to enforce rate limits on specific tools","I want to transform tool inputs/outputs (e.g., sanitize, redact)"],"best_for":["Production MCP servers requiring observability and compliance","Teams implementing rate limiting or access control","Applications needing input/output transformation"],"limitations":["Middleware execution adds latency (~10-50ms per middleware); no built-in optimization","Error handling in middleware is manual; no built-in fallback patterns","Middleware order is implicit (registration order); no explicit dependency management"],"requires":["Python 3.9+","arcade-mcp-server with middleware support","Async-compatible middleware for non-blocking operations"],"input_types":["Tool invocation request","Tool execution context"],"output_types":["Transformed request/response","Logged/audited invocation data"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-arcadeai-arcade-mcp__cap_8","uri":"capability://memory.knowledge.session.management.and.stateful.tool.execution","name":"session management and stateful tool execution","description":"Provides session context management allowing tools to maintain state across multiple invocations. Sessions are identified by session ID and can store arbitrary data (user context, conversation history, tool state). Tools access session data via context injection; framework handles session lifecycle (creation, persistence, cleanup). Supports in-memory and persistent backends.","intents":["I want tools to remember user context across multiple invocations","I need to maintain conversation history for multi-turn interactions","I want to store tool state (e.g., authenticated user) between calls"],"best_for":["Multi-turn agent applications requiring context persistence","Teams building stateful tool interactions","Applications needing user-scoped tool execution"],"limitations":["In-memory sessions are lost on server restart; persistent backend required for production","Session storage is not provided; requires external database or cache (Redis, etc.)","Session cleanup is manual; no automatic expiration or garbage collection"],"requires":["Python 3.9+","arcade-mcp-server with session support","For persistence: external storage (Redis, database, etc.)"],"input_types":["Session ID","Session data (arbitrary JSON)"],"output_types":["Session context injected into tools","Updated session state"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-arcadeai-arcade-mcp__cap_9","uri":"capability://automation.workflow.notifications.and.event.streaming.system","name":"notifications and event streaming system","description":"Implements a notification system allowing tools to emit events (progress updates, alerts, notifications) that are streamed to clients. Uses Server-Sent Events (SSE) for HTTP transport and custom message framing for stdio. Tools emit notifications via context API; framework handles delivery and client subscription. Supports event filtering and prioritization.","intents":["I want tools to send progress updates during long-running operations","I need to stream alerts or notifications to clients in real-time","I want to implement cancellable tool execution with status updates"],"best_for":["Long-running tool operations requiring progress feedback","Real-time agent applications with streaming updates","Teams building interactive tool UIs"],"limitations":["Stdio transport has limited streaming capability; SSE is more reliable","Event delivery is best-effort; no guaranteed delivery or persistence","Client-side subscription handling is manual; no built-in UI components"],"requires":["Python 3.9+","arcade-mcp-server with notifications support","HTTP transport (SSE) or compatible stdio client"],"input_types":["Notification event (type, message, metadata)"],"output_types":["Server-Sent Events stream (HTTP)","Custom messages (stdio)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","arcade-mcp-server package installed","Type hints on function parameters for optimal schema generation","arcade-mcp-server with transport modules","For HTTP: web server framework (FastAPI/Starlette)","For stdio: compatible MCP client (Claude Desktop 0.1+, Cursor 0.42+)","arcade-mcp-server with usage tracking","Optional: monitoring system (Prometheus, CloudWatch, etc.)","arcade-mcp-server with resources/prompts support","arcade-mcp-server with error handling"],"failure_modes":["Python-only implementation; no native support for other languages","Schema generation relies on type hints; untyped functions produce generic schemas","Decorator-based registration happens at import time, limiting dynamic tool loading","HTTP transport requires explicit SSE configuration; standard REST clients cannot consume MCP protocol directly","Stdio transport is blocking; high-concurrency scenarios may require process pooling","Transport selection is compile-time; cannot dynamically switch transports per connection","Metrics are in-memory; no persistence across restarts without export","Export to external systems requires custom integration","Real-time alerting is not built-in; requires external monitoring","Resources are read-only in current implementation; no write support","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4100080125963291,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T14:23:34.856Z","last_commit":"2026-05-01T23:15:30Z"},"community":{"stars":877,"forks":89,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-arcadeai-arcade-mcp","compare_url":"https://unfragile.ai/compare?artifact=mcp-arcadeai-arcade-mcp"}},"signature":"ShswBNcBVS/DmH/EDPIhUZqHnbTUPv7s1SejO5QQqa1VJ5lgdcSSYYToYQTa1eTDP3n0+N/EgAp9MsbLgqIaAQ==","signedAt":"2026-06-22T04:33:55.445Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-arcadeai-arcade-mcp","artifact":"https://unfragile.ai/mcp-arcadeai-arcade-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-arcadeai-arcade-mcp","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"}}