{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-a2a-mcp-java-bridge","slug":"a2a-mcp-java-bridge","name":"A2A-MCP Java Bridge","type":"mcp","url":"https://github.com/vishalmysore/a2ajava","page_url":"https://unfragile.ai/a2a-mcp-java-bridge","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-a2a-mcp-java-bridge__cap_0","uri":"capability://tool.use.integration.annotation.driven.dual.protocol.action.exposure.a2a.mcp","name":"annotation-driven dual-protocol action exposure (a2a + mcp)","description":"Developers annotate standard Java methods with @Action, @Agent, and @ActionParameter decorators; the framework's PredictionLoader package scanner automatically introspects these annotations at startup and registers the same method as both an A2A Skill (discoverable at /.well-known/agent.json) and an MCP Tool (via tools/list endpoint). A unified AIProcessor orchestrates invocation through both protocols without code duplication, using protocol-specific controllers (DynamicTaskController for A2A, MCPToolsController for MCP) that delegate to the same underlying business logic.","intents":["Write agent methods once and expose them to both Gemini (A2A) and Claude (MCP) clients simultaneously without protocol-specific boilerplate","Automatically generate agent capability manifests for discovery without manual service registration","Build multi-protocol agent applications with minimal annotation overhead"],"best_for":["Java teams building multi-LLM agent applications targeting both Google and Anthropic ecosystems","Developers who want write-once, deploy-everywhere agent logic across A2A and MCP protocols"],"limitations":["Annotation processing happens at startup — runtime method registration not supported","Protocol-specific parameter transformations must be handled via custom ActionParameter annotations; no automatic type coercion between A2A and MCP schemas","Requires Spring Boot context for component scanning and dependency injection"],"requires":["Java 11+","Spring Boot 2.7+ or 3.0+","Maven or Gradle with annotation processing enabled","At least one AI provider SDK (Gemini, OpenAI, or Anthropic) on classpath"],"input_types":["Java method signatures with primitive/object parameters","Annotation metadata (@Action, @ActionParameter with descriptions and types)"],"output_types":["A2A Skill JSON schema in /.well-known/agent.json","MCP Tool definitions via JSON-RPC tools/list response","Unified invocation results routed back to caller protocol"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-a2a-mcp-java-bridge__cap_1","uri":"capability://tool.use.integration.multi.provider.ai.processor.abstraction.with.protocol.specific.implementations","name":"multi-provider ai processor abstraction with protocol-specific implementations","description":"The AIProcessor interface abstracts LLM invocation across Gemini, OpenAI, and Anthropic, with concrete implementations (GeminiV2ActionProcessor, OpenAiActionProcessor, AnthropicActionProcessor) that handle provider-specific request/response formatting, streaming, and tool-calling conventions. The framework selects the appropriate processor at runtime based on configuration, allowing a single @Action method to be invoked by different LLM providers without code changes. Integration with tools4ai library enables structured tool-calling across all providers.","intents":["Support Gemini, OpenAI, and Anthropic LLMs interchangeably without rewriting agent logic","Delegate provider-specific tool-calling semantics (function calling, tool use, etc.) to pluggable processors","Enable multi-LLM voting or fallback strategies by routing the same action through multiple providers"],"best_for":["Teams evaluating multiple LLM providers and wanting to avoid vendor lock-in","Enterprises requiring multi-LLM redundancy or comparative analysis","Developers building cost-optimized agents that switch providers based on task complexity"],"limitations":["Provider-specific features (e.g., Gemini's dynamic retrieval, Claude's extended thinking) require custom processor implementations","Streaming response handling differs per provider; SSEEmitterCallback abstracts this but may lose provider-specific metadata","No automatic fallback between providers on failure — must be implemented in custom orchestration logic"],"requires":["API keys for at least one of: Google Gemini, OpenAI, Anthropic","Provider SDKs on classpath (google-generativeai, openai-java, anthropic-sdk-java)","Network access to provider endpoints"],"input_types":["AIProcessor interface contract (action name, parameters, context)","Provider-specific request objects (GenerateContentRequest, ChatCompletionRequest, etc.)"],"output_types":["Unified action response objects (SendTaskResponse)","Streaming events via SSEEmitterCallback for long-running operations","Tool-calling results routed back to provider for agentic loops"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-a2a-mcp-java-bridge__cap_10","uri":"capability://automation.workflow.custom.action.callbacks.and.protocol.extension.hooks","name":"custom action callbacks and protocol extension hooks","description":"The framework provides ActionCallback interface and custom callback implementations (SSEEmitterCallback, etc.) that allow developers to hook into action execution lifecycle (before, during, after) and extend protocol behavior. Callbacks receive execution context including action name, parameters, and results, enabling custom logging, monitoring, authorization, and result transformation. Protocol extensions can be implemented by subclassing controller classes and overriding request/response handling, allowing teams to add custom headers, authentication schemes, or result formatting without modifying core framework code.","intents":["Implement custom logging and monitoring for agent actions without modifying business logic","Add authorization/authentication checks to action execution","Transform or enrich action results before returning to clients","Extend protocol behavior (e.g., custom A2A headers, MCP extensions)"],"best_for":["Teams requiring custom observability/monitoring for agent actions","Enterprises with authorization requirements (role-based access control, attribute-based access control)","Developers building agent platforms that need extensibility hooks"],"limitations":["Callback execution adds latency; high-overhead callbacks may impact action performance","No built-in callback ordering or dependency management; complex callback chains must be manually orchestrated","Protocol extensions require understanding of framework internals (controller classes, request/response objects)","Callbacks are synchronous; asynchronous callbacks (e.g., async logging) require manual threading"],"requires":["Implementation of ActionCallback interface","Registration of callbacks in Spring context or via configuration","Understanding of action execution lifecycle and protocol request/response models"],"input_types":["ActionCallback implementations with before/during/after hooks","Custom controller subclasses for protocol extensions"],"output_types":["Callback execution results (logs, metrics, authorization decisions)","Extended protocol responses with custom headers or fields"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-a2a-mcp-java-bridge__cap_11","uri":"capability://automation.workflow.testing.framework.with.a2a.and.mcp.client.test.utilities","name":"testing framework with a2a and mcp client test utilities","description":"The framework provides A2ATaskClientTest and regression test utilities that enable developers to test agent actions via both A2A and MCP protocols without deploying to a server. Test utilities include mock clients (A2AAgent, MCPAgent) that invoke actions directly, assertion helpers for validating results, and fixtures for common test scenarios. The testing framework integrates with Spring Boot Test, allowing agents to be tested in isolation with mocked LLM providers or real providers depending on test requirements.","intents":["Unit test agent actions without deploying to a server","Test both A2A and MCP protocol paths in the same test suite","Validate action behavior with different LLM providers (Gemini, OpenAI, Anthropic)"],"best_for":["Teams building agent applications with high test coverage requirements","Developers who want to test protocol behavior without integration tests","Enterprises requiring regression testing for agent behavior changes"],"limitations":["Testing with real LLM providers requires API keys and incurs costs","Mock LLM providers may not accurately simulate real provider behavior (e.g., error handling, rate limiting)","Streaming behavior (SSE) is difficult to test; test utilities may not fully simulate streaming","Multi-agent routing and orchestration testing requires complex test setup"],"requires":["Spring Boot Test framework","JUnit 4 or 5","Test fixtures and mock implementations from framework"],"input_types":["Test cases with action invocations and expected results","Mock or real LLM provider configurations"],"output_types":["Test results with pass/fail status","Assertion errors with detailed failure messages"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-a2a-mcp-java-bridge__cap_12","uri":"capability://automation.workflow.dynamic.task.controller.with.asynchronous.execution.and.polling","name":"dynamic task controller with asynchronous execution and polling","description":"The DynamicTaskController implements asynchronous task execution for long-running @Action methods, assigning unique task IDs and allowing clients to poll for completion status via REST endpoints. Task state is tracked in memory (or can be persisted to external storage), with endpoints for task creation (/task/create), status polling (/task/status/{taskId}), and result retrieval (/task/result/{taskId}). This enables non-blocking client interactions where clients submit tasks and check back later, rather than blocking on action execution. The controller integrates with SSEEmitterCallback for streaming intermediate results during task execution.","intents":["Execute long-running agent actions without blocking HTTP connections","Allow clients to submit tasks and retrieve results asynchronously","Provide task status visibility for long-running operations"],"best_for":["Web applications with long-running agent operations (>30 seconds)","Mobile clients that cannot maintain long-lived HTTP connections","Batch processing systems that submit multiple tasks and retrieve results later"],"limitations":["Task state is stored in memory by default; no persistence across application restarts","Polling model adds latency compared to callback-based completion notification","No built-in task cleanup; old tasks accumulate in memory unless explicitly purged","Concurrent task execution is limited by application thread pool; high task volume may cause queueing"],"requires":["DynamicTaskController registered in Spring","Configured thread pool for task execution","Optional: external storage for task state persistence"],"input_types":["Task creation requests with action name and parameters","Task ID for status/result polling"],"output_types":["Task ID for tracking","Task status (pending, running, completed, failed)","Task result with output data"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-a2a-mcp-java-bridge__cap_2","uri":"capability://automation.workflow.real.time.streaming.with.sse.callbacks.for.long.running.agent.operations","name":"real-time streaming with sse callbacks for long-running agent operations","description":"The SSEEmitterCallback and SseEmitter components enable Server-Sent Events streaming for long-running @Action methods, allowing clients to receive intermediate results and status updates without blocking. The framework wraps action execution in a streaming context that captures callbacks and pushes them to HTTP clients via Spring's SseEmitter, with protocol-aware formatting for both A2A and MCP consumers. This enables interactive agent experiences where users see progress in real-time rather than waiting for final results.","intents":["Stream intermediate results from long-running agent tasks (e.g., multi-step reasoning, document processing) to clients in real-time","Provide progress visibility for expensive operations without blocking the HTTP connection","Build interactive agent UIs that update as the agent thinks or acts"],"best_for":["Web-based agent applications requiring real-time user feedback","Multi-step reasoning tasks where intermediate steps are valuable to expose","Teams building agent dashboards or monitoring interfaces"],"limitations":["SSE requires HTTP/1.1 or HTTP/2 with streaming support; not compatible with HTTP/2 server push in all environments","Callback overhead adds latency per event; high-frequency callbacks (>100/sec) may saturate connections","Client-side SSE handling required; not all JavaScript frameworks have native SSE support","No built-in retry or reconnection logic — clients must implement exponential backoff"],"requires":["Spring Boot 2.7+ with servlet container supporting async I/O","Client-side EventSource API or SSE library (e.g., eventsource npm package)","Network path allowing long-lived HTTP connections (may require proxy/load-balancer configuration)"],"input_types":["Action method execution context","Callback events (progress, intermediate results, status messages)"],"output_types":["Server-Sent Events stream (text/event-stream MIME type)","JSON-formatted event payloads with event type and data fields"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-a2a-mcp-java-bridge__cap_3","uri":"capability://planning.reasoning.agenticmesh.multi.agent.orchestration.with.ai.powered.routing","name":"agenticmesh multi-agent orchestration with ai-powered routing","description":"The AgenticMesh class implements multi-agent orchestration patterns where multiple @Agent instances are registered in an AgentCatalog, and incoming requests are routed to the most appropriate agent based on AI-powered selection logic. The framework uses the configured LLM provider to analyze request intent and select the best agent, then delegates execution to that agent's actions. This enables hierarchical agent systems where a coordinator agent can decompose tasks and route sub-tasks to specialist agents, with all routing decisions made by the LLM rather than hardcoded rules.","intents":["Route incoming agent requests to the most appropriate specialist agent based on task intent","Build hierarchical agent systems where a coordinator delegates to multiple specialist agents","Dynamically select agents at runtime without hardcoded routing rules"],"best_for":["Large-scale agent systems with multiple specialized agents (e.g., customer service, content generation, data analysis)","Teams building agent orchestration platforms or agent-as-a-service offerings","Applications requiring dynamic agent selection based on semantic task understanding"],"limitations":["AI-powered routing adds latency (typically 200-500ms per routing decision) — not suitable for sub-100ms latency requirements","Routing decisions depend on LLM quality; poor agent descriptions in AgentCard lead to misrouting","No built-in load balancing or capacity awareness — all agents treated equally regardless of current load","Circular routing (agent A routes to agent B which routes back to A) must be prevented by developer logic"],"requires":["Multiple @Agent instances registered in Spring context","AgentCatalog populated with agent metadata (names, descriptions, capabilities)","Configured AIProcessor for routing decisions","Agent descriptions detailed enough for LLM to disambiguate"],"input_types":["Incoming task request (text or structured)","AgentCard metadata for all registered agents"],"output_types":["Selected agent identifier","Routed request to chosen agent's action handler","Final result from selected agent"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-a2a-mcp-java-bridge__cap_4","uri":"capability://tool.use.integration.a2a.protocol.task.lifecycle.management.with.skill.discovery","name":"a2a protocol task lifecycle management with skill discovery","description":"The DynamicTaskController implements the A2A (Agent-to-Agent) protocol task lifecycle, handling task creation, status polling, and result retrieval through REST endpoints. The framework automatically generates A2A Skill definitions from @Action annotations and exposes them at /.well-known/agent.json for discovery by A2A-compatible clients (e.g., Gemini agents). Task execution is tracked with unique task IDs, allowing asynchronous clients to poll for completion status and retrieve results without blocking, with support for long-running operations via SSE streaming.","intents":["Expose Java agent actions as A2A Skills discoverable by Gemini and other A2A-compatible agents","Implement asynchronous task execution with polling-based status checks for long-running operations","Enable agent-to-agent communication where Gemini agents can discover and invoke Java agent capabilities"],"best_for":["Teams building Gemini agent applications that need to invoke Java backend services","Enterprises integrating Java microservices with Google's agentic AI platform","Developers creating agent-to-agent communication networks across heterogeneous systems"],"limitations":["A2A protocol is Google-specific; no support for other agent frameworks (e.g., OpenAI agents, Anthropic agents)","Task polling model adds latency compared to callback-based completion notification","Skill discovery at /.well-known/agent.json is static (generated at startup); dynamic skill registration not supported","No built-in authentication/authorization — must be layered via Spring Security or custom filters"],"requires":["Spring Boot application with DynamicTaskController registered","A2A-compatible client (Gemini agent, A2A SDK)","Network accessibility of /.well-known/agent.json endpoint","Configured AIProcessor for task execution"],"input_types":["@Action annotations with A2A-compatible parameter types","A2A task requests (JSON with task name and parameters)"],"output_types":["A2A Skill JSON schema at /.well-known/agent.json","Task creation response with task ID","Task status responses (pending, running, completed, failed)","Task result with output data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-a2a-mcp-java-bridge__cap_5","uri":"capability://tool.use.integration.mcp.protocol.tool.invocation.with.json.rpc.gateway","name":"mcp protocol tool invocation with json-rpc gateway","description":"The MCPToolsController and JSON-RPC Controller Gateway implement the MCP (Model Context Protocol) server specification, exposing @Action methods as MCP Tools via JSON-RPC 2.0 endpoints. The framework handles MCP tool listing (tools/list), tool invocation (tools/call), and result serialization, with automatic schema generation from @ActionParameter annotations. The JSON-RPC gateway routes incoming MCP requests to the appropriate controller, handling protocol-level concerns (request/response formatting, error handling) while delegating business logic to the unified AIProcessor.","intents":["Expose Java agent actions as MCP Tools compatible with Claude, OpenAI, and other MCP-supporting clients","Implement MCP server specification without manual JSON-RPC boilerplate","Enable Claude and other LLMs to discover and invoke Java backend capabilities via MCP"],"best_for":["Teams building Claude-integrated applications that need Java backend services","Enterprises standardizing on MCP for LLM tool integration across multiple providers","Developers creating MCP servers that expose existing Java business logic"],"limitations":["MCP tool schemas must be JSON-serializable; complex Java objects require custom serialization via @ActionParameter annotations","No built-in support for streaming tool results in MCP (protocol limitation) — long-running operations must complete before returning","Tool parameter validation happens at MCP level; no cross-parameter validation or conditional requirements","MCP client connection management (stdio vs HTTP) must be handled by deployment layer"],"requires":["Spring Boot application with MCPToolsController registered","MCP-compatible client (Claude, OpenAI with MCP support, or MCP SDK)","JSON-RPC 2.0 compatible HTTP or stdio transport","Configured AIProcessor for tool execution"],"input_types":["@Action annotations with MCP-compatible parameter types (strings, numbers, booleans, objects)","JSON-RPC 2.0 tool invocation requests with method name and parameters"],"output_types":["MCP tools/list response with tool definitions and schemas","MCP tool invocation results (JSON-serialized return values)","JSON-RPC error responses for invalid invocations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-a2a-mcp-java-bridge__cap_6","uri":"capability://memory.knowledge.agent.capability.metadata.and.agentcard.generation","name":"agent capability metadata and agentcard generation","description":"The AgentCard class and DynamicAgentCardController automatically generate capability metadata from @Agent and @Action annotations, exposing agent descriptions, action names, parameter schemas, and return types. This metadata is used for agent discovery (in AgentCatalog), multi-agent routing (in AgenticMesh), and protocol-level schema generation (for A2A and MCP). The framework introspects Java method signatures and annotation values to build rich capability descriptions without manual metadata definition.","intents":["Automatically generate agent capability manifests from Java annotations without manual schema writing","Enable agent discovery and routing by exposing structured capability metadata","Build agent catalogs and capability registries for multi-agent systems"],"best_for":["Teams building multi-agent systems that need dynamic agent discovery","Developers who want to avoid maintaining separate capability documentation","Applications requiring agent capability introspection at runtime"],"limitations":["Metadata generation happens at startup; runtime capability changes not reflected without restart","Complex Java types (generics, custom objects) may not serialize cleanly to capability schemas","No built-in versioning of agent capabilities; breaking changes require manual migration logic","AgentCard descriptions depend on annotation quality; poor @Agent/@Action descriptions produce poor capability metadata"],"requires":["@Agent and @Action annotations on Java classes/methods","Spring Boot context for component scanning","DynamicAgentCardController registered in Spring"],"input_types":["@Agent annotation with name and description","@Action annotations with parameter and return type metadata"],"output_types":["AgentCard objects with capability metadata","JSON capability schemas for protocol-level exposure","Agent descriptions for routing and discovery"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-a2a-mcp-java-bridge__cap_7","uri":"capability://tool.use.integration.cross.protocol.a2ui.component.builder.system","name":"cross-protocol a2ui component builder system","description":"The A2UI Extension System provides component builders that generate UI schemas compatible with both A2A and MCP protocols, allowing @Action methods to specify rich UI hints (dropdowns, date pickers, text areas) that render correctly in both Gemini and Claude clients. The framework abstracts protocol-specific UI representation differences, translating a single A2UI component definition into both A2A and MCP UI schemas. This enables agent developers to define rich UIs once and have them work across multiple LLM platforms.","intents":["Define rich UI components for agent actions that render correctly in both Gemini and Claude interfaces","Avoid duplicating UI specifications for A2A and MCP protocols","Build agent applications with consistent UX across multiple LLM platforms"],"best_for":["Teams building agent applications for multiple LLM platforms (Gemini + Claude)","Developers who want to provide rich UI hints without protocol-specific boilerplate","Applications requiring consistent agent UX across heterogeneous clients"],"limitations":["A2UI component support depends on client implementation; not all Gemini/Claude clients support all component types","Complex UI logic (conditional visibility, dynamic options) must be implemented client-side","No built-in validation of UI schemas against protocol specifications","Component builder API may lag behind protocol updates"],"requires":["@ActionParameter annotations with A2UI component hints","A2UI component builder classes in classpath","Client support for A2UI components (Gemini agent, Claude client)"],"input_types":["A2UI component builder specifications (e.g., dropdown options, date range constraints)"],"output_types":["A2A-compatible UI schema","MCP-compatible UI schema","Protocol-agnostic component definitions"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-a2a-mcp-java-bridge__cap_8","uri":"capability://tool.use.integration.multi.language.client.support.java.node.js.python.with.protocol.bridges","name":"multi-language client support (java, node.js, python) with protocol bridges","description":"The framework provides native client libraries for Java (A2AAgent, MCPAgent), Node.js (stdio bridge via mcpserver.js), and Python (MCP client adapters) that enable applications in different languages to invoke A2A and MCP servers. The Java clients use the same annotation model as servers, allowing symmetric agent development. The Node.js and Python clients implement protocol-specific adapters that handle JSON-RPC marshaling and protocol state management, enabling polyglot agent systems where agents written in different languages can communicate via A2A or MCP.","intents":["Invoke A2A and MCP servers from Java, Node.js, or Python applications without protocol boilerplate","Build polyglot agent systems where agents in different languages communicate via standard protocols","Reuse agent logic across multiple technology stacks"],"best_for":["Polyglot teams with services in Java, Node.js, and Python that need to share agent capabilities","Enterprises migrating from monolithic Java to microservices with agents in multiple languages","Developers building agent platforms that support multiple language runtimes"],"limitations":["Java client requires Spring Boot context; not suitable for standalone scripts or serverless functions","Node.js and Python clients require manual protocol implementation; less integrated than Java client","Type safety varies by language; Python client has no static type checking for agent responses","Cross-language debugging is complex; errors in one language may manifest as protocol errors in another"],"requires":["For Java: Spring Boot 2.7+, A2AAgent or MCPAgent classes","For Node.js: Node.js 14+, mcpserver.js bridge, stdio transport","For Python: Python 3.8+, MCP SDK, network access to server"],"input_types":["Agent method invocations (Java annotations, Node.js function calls, Python method calls)","Protocol-specific request objects (A2A task requests, MCP tool invocations)"],"output_types":["Agent results (Java objects, Node.js promises, Python dicts)","Protocol-level responses (A2A task results, MCP tool results)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-a2a-mcp-java-bridge__cap_9","uri":"capability://automation.workflow.spring.boot.integration.with.properties.based.configuration","name":"spring boot integration with properties-based configuration","description":"The framework integrates deeply with Spring Boot via @EnableAgent annotation and properties-based configuration (application.properties or application.yml), allowing developers to configure AI providers, protocol settings, and agent behavior without code changes. Spring's component scanning automatically discovers @Agent and @Action classes, registers them as beans, and wires them into controllers. Properties like a2a.enabled, mcp.enabled, and ai.provider control which protocols and LLM providers are active, enabling environment-specific configuration (e.g., Gemini in production, OpenAI in development).","intents":["Configure agent behavior and AI providers via Spring properties without code changes","Enable/disable A2A and MCP protocols per environment","Switch between LLM providers (Gemini, OpenAI, Anthropic) via configuration"],"best_for":["Spring Boot teams who want agent configuration to follow Spring conventions","Enterprises requiring environment-specific agent configuration (dev/staging/prod)","Teams using Spring Cloud Config or other centralized configuration systems"],"limitations":["Configuration is static at startup; runtime changes require application restart","Complex orchestration logic (e.g., conditional routing based on request attributes) must be implemented in code, not configuration","Property validation happens at startup; invalid configurations may not be caught until runtime","No built-in support for secrets management; API keys must be injected via environment variables or Spring Cloud Vault"],"requires":["Spring Boot 2.7+ or 3.0+","@EnableAgent annotation on Spring Boot application class","application.properties or application.yml with agent configuration"],"input_types":["Spring properties (a2a.enabled, mcp.enabled, ai.provider, etc.)","Environment variables for sensitive data (API keys)"],"output_types":["Configured Spring beans for agents, controllers, and processors","Active protocol endpoints (A2A, MCP) based on configuration"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Java 11+","Spring Boot 2.7+ or 3.0+","Maven or Gradle with annotation processing enabled","At least one AI provider SDK (Gemini, OpenAI, or Anthropic) on classpath","API keys for at least one of: Google Gemini, OpenAI, Anthropic","Provider SDKs on classpath (google-generativeai, openai-java, anthropic-sdk-java)","Network access to provider endpoints","Implementation of ActionCallback interface","Registration of callbacks in Spring context or via configuration","Understanding of action execution lifecycle and protocol request/response models"],"failure_modes":["Annotation processing happens at startup — runtime method registration not supported","Protocol-specific parameter transformations must be handled via custom ActionParameter annotations; no automatic type coercion between A2A and MCP schemas","Requires Spring Boot context for component scanning and dependency injection","Provider-specific features (e.g., Gemini's dynamic retrieval, Claude's extended thinking) require custom processor implementations","Streaming response handling differs per provider; SSEEmitterCallback abstracts this but may lose provider-specific metadata","No automatic fallback between providers on failure — must be implemented in custom orchestration logic","Callback execution adds latency; high-overhead callbacks may impact action performance","No built-in callback ordering or dependency management; complex callback chains must be manually orchestrated","Protocol extensions require understanding of framework internals (controller classes, request/response objects)","Callbacks are synchronous; asynchronous callbacks (e.g., async logging) require manual threading","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.370Z","last_scraped_at":"2026-05-03T14:00:18.053Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=a2a-mcp-java-bridge","compare_url":"https://unfragile.ai/compare?artifact=a2a-mcp-java-bridge"}},"signature":"gOvXr4W3Mqxb8Z80lQy2hRqVeb8X9vtK0SeoyP9JfKf+HgAYj6HeJtJrK456NNe3WTdqLwFDGZf10EOTvzU0Cg==","signedAt":"2026-06-20T14:54:29.336Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/a2a-mcp-java-bridge","artifact":"https://unfragile.ai/a2a-mcp-java-bridge","verify":"https://unfragile.ai/api/v1/verify?slug=a2a-mcp-java-bridge","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"}}