{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-mcp-bridge","slug":"mcp-bridge","name":"MCP-Bridge","type":"mcp","url":"https://github.com/SecretiveShell/MCP-Bridge","page_url":"https://unfragile.ai/mcp-bridge","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-mcp-bridge__cap_0","uri":"capability://tool.use.integration.openai.api.compatible.endpoint.proxying.with.mcp.tool.injection","name":"openai api-compatible endpoint proxying with mcp tool injection","description":"MCP-Bridge exposes FastAPI endpoints that implement the OpenAI chat completions API specification, intercepting incoming requests and dynamically injecting available MCP tool definitions into the request payload before forwarding to downstream LLM inference servers. This allows any OpenAI-compatible client (Claude Desktop, LM Studio, Ollama, etc.) to transparently access MCP tools without modification. The middleware performs request transformation at the HTTP layer, mapping between OpenAI tool schemas and MCP tool schemas bidirectionally.","intents":["Use existing OpenAI API clients with MCP tools without rewriting client code","Add tool-calling capabilities to local inference servers that only support OpenAI API format","Create a unified tool interface across multiple MCP servers behind a single OpenAI-compatible endpoint","Enable non-MCP-aware applications to leverage MCP ecosystem tools"],"best_for":["Teams with existing OpenAI API client integrations wanting to adopt MCP tools","Developers running local LLM inference servers (Ollama, LM Studio, vLLM) needing tool support","Organizations building tool-calling features without rewriting client applications"],"limitations":["Adds one additional network hop and request transformation overhead (~50-100ms per request)","Requires downstream inference server to support OpenAI function-calling API format","Tool schema translation may lose MCP-specific metadata not representable in OpenAI schema","No built-in caching of tool definitions — refreshes on each request cycle"],"requires":["Python 3.9+","FastAPI 0.100+","OpenAI API-compatible inference server (local or remote)","One or more MCP servers configured and accessible"],"input_types":["JSON (OpenAI chat completions request format with messages array)","Tool definitions from MCP servers (JSON schema format)"],"output_types":["JSON (OpenAI chat completions response format with tool_calls)","Server-Sent Events stream (for streaming responses)"],"categories":["tool-use-integration","api-compatibility-layer"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-bridge__cap_1","uri":"capability://tool.use.integration.dynamic.mcp.server.connection.management.with.multi.server.support","name":"dynamic mcp server connection management with multi-server support","description":"MCP-Bridge maintains a configurable pool of connections to multiple MCP servers, handling lifecycle management (connection establishment, health checks, reconnection on failure) through an MCP Client Manager component. The system discovers available tools from each connected MCP server, aggregates their tool definitions, and maintains a unified tool registry. Connection configuration is typically specified via environment variables or configuration files, allowing runtime addition/removal of MCP servers without code changes.","intents":["Connect to multiple MCP tool servers and aggregate their capabilities into a single interface","Automatically discover and register tools from MCP servers without manual configuration","Handle MCP server failures gracefully with automatic reconnection logic","Manage the lifecycle of MCP server connections (startup, shutdown, health monitoring)"],"best_for":["Deployments requiring access to tools from multiple specialized MCP servers","Teams wanting to add/remove MCP servers without restarting the bridge","Production environments needing resilient connections to external tool providers"],"limitations":["Connection pooling adds memory overhead proportional to number of MCP servers","Tool discovery happens at startup or on manual refresh — no hot-reloading of tool definitions","No built-in load balancing across multiple instances of the same MCP server","Reconnection backoff strategy is fixed — no configurable exponential backoff parameters exposed"],"requires":["Python 3.9+","MCP server implementations accessible via stdio or HTTP transport","Configuration specifying MCP server endpoints (environment variables or config file)"],"input_types":["MCP server configuration (endpoint URLs, transport type, authentication)","Tool discovery requests to MCP servers"],"output_types":["Aggregated tool registry (list of available tools from all connected servers)","Tool execution results routed back from MCP servers"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-bridge__cap_10","uri":"capability://automation.workflow.release.process.and.version.management","name":"release process and version management","description":"MCP-Bridge includes a structured release process with version tagging and release notes. The project uses semantic versioning and maintains a changelog documenting changes across releases. Release artifacts are published to package registries (PyPI, GitHub Releases, etc.), allowing users to install specific versions. The release process is automated via CI/CD pipelines that build, test, and publish releases.","intents":["Track changes and features across bridge versions","Install specific versions of MCP-Bridge for stability and compatibility","Understand breaking changes before upgrading","Manage dependencies on specific bridge versions"],"best_for":["Production deployments requiring stable, versioned releases","Teams wanting to track and manage bridge upgrades","Projects that depend on MCP-Bridge as a library"],"limitations":["Release frequency depends on maintainer availability","No long-term support (LTS) versions — all versions have same support duration","Breaking changes may require application updates","No automated security patching — users must manually upgrade"],"requires":["Package manager (pip, Docker, etc.) for installing releases","Knowledge of semantic versioning for understanding compatibility"],"input_types":["Release version tags and changelog"],"output_types":["Published release artifacts (Python packages, Docker images)","Release notes documenting changes"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-bridge__cap_2","uri":"capability://tool.use.integration.bidirectional.tool.schema.translation.between.openai.and.mcp.formats","name":"bidirectional tool schema translation between openai and mcp formats","description":"MCP-Bridge implements a tool mapping layer that converts MCP tool definitions (with MCP-specific schema format) into OpenAI function-calling schema format for injection into requests, and conversely translates OpenAI tool_call objects back into MCP-compatible tool invocation requests. This translation handles differences in schema representation, parameter validation rules, and response formatting between the two protocols, ensuring semantic equivalence despite format differences.","intents":["Convert MCP tool schemas to OpenAI function format for injection into chat completion requests","Translate OpenAI tool_call responses back into MCP tool invocation format for execution","Maintain semantic correctness of tool definitions across protocol boundaries","Handle schema differences in parameter types, descriptions, and validation rules"],"best_for":["Integrations requiring transparent protocol translation without application awareness","Teams using MCP tools with OpenAI API clients that don't natively support MCP","Scenarios where tool definitions must be preserved across multiple protocol conversions"],"limitations":["MCP-specific metadata not representable in OpenAI schema may be lost during translation","Complex nested parameter types may not translate perfectly between schema formats","No validation that translated schemas are semantically equivalent — relies on correct implementation","Tool response formatting differences may require post-processing to match expected output format"],"requires":["Python 3.9+","Knowledge of both OpenAI function-calling schema and MCP tool definition formats","Proper error handling for schema translation failures"],"input_types":["MCP tool definitions (JSON schema with MCP-specific structure)","OpenAI tool_call objects (with function name and arguments)"],"output_types":["OpenAI-format function definitions (for injection into chat requests)","MCP tool invocation requests (for routing to MCP servers)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-bridge__cap_3","uri":"capability://tool.use.integration.tool.call.routing.and.execution.with.mcp.server.dispatch","name":"tool call routing and execution with mcp server dispatch","description":"When an LLM generates tool_call objects in response to a chat completion request, MCP-Bridge intercepts these calls, identifies which MCP server should handle each tool, routes the invocation to the appropriate server, and collects results. The system maintains a mapping of tool names to their source MCP servers, enabling correct dispatch even when multiple servers provide tools with similar names. Tool execution is synchronous with request processing, and results are formatted back into OpenAI API response format.","intents":["Route tool calls from LLM responses to the correct MCP server for execution","Execute tools on behalf of the LLM and collect results for context injection","Handle tool execution errors gracefully and return error information to the LLM","Support multi-turn conversations where tool results inform subsequent LLM responses"],"best_for":["Agentic workflows where LLMs iteratively call tools to accomplish tasks","Applications requiring tool execution as part of the chat completion flow","Scenarios where tool results must be fed back to the LLM for further reasoning"],"limitations":["Tool execution is synchronous — long-running tools block the request until completion","No built-in timeout mechanism for tool execution — relies on MCP server timeouts","Tool execution errors may not be properly formatted for LLM consumption","No support for parallel tool execution — tools are invoked sequentially"],"requires":["Python 3.9+","MCP servers with tool implementations accessible and responsive","Proper error handling in MCP servers for malformed tool invocations"],"input_types":["OpenAI tool_call objects (function name and arguments)","Tool name to MCP server mapping"],"output_types":["Tool execution results (JSON or text)","Error messages if tool execution fails","Formatted tool results for injection into chat context"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-bridge__cap_4","uri":"capability://tool.use.integration.streaming.chat.completion.responses.with.server.sent.events.bridge","name":"streaming chat completion responses with server-sent events bridge","description":"MCP-Bridge supports both streaming and non-streaming chat completion responses. For streaming requests, it implements a Server-Sent Events (SSE) interface that forwards LLM token streams to clients while managing tool calls that may occur mid-stream. The system buffers tool calls, executes them when complete, and injects results back into the stream context. This enables real-time token delivery while maintaining tool-calling semantics.","intents":["Stream LLM responses token-by-token for real-time user feedback","Handle tool calls that occur during streaming without breaking the stream","Provide SSE interface for clients that need real-time event-driven responses","Support both streaming and non-streaming clients with a single endpoint"],"best_for":["Interactive applications requiring real-time token streaming","Web applications using Server-Sent Events for real-time updates","Scenarios where tool calls may occur mid-stream and results must be injected"],"limitations":["Streaming with tool calls adds complexity — tool results must be buffered and injected","SSE interface requires client support for event-driven streaming","Tool execution during streaming may introduce latency gaps in token delivery","No support for client-side cancellation of in-flight tool executions"],"requires":["Python 3.9+","FastAPI with streaming response support","Client support for Server-Sent Events or streaming HTTP responses"],"input_types":["OpenAI chat completion request with stream=true parameter","Tool definitions for potential mid-stream tool calls"],"output_types":["Server-Sent Events stream with delta tokens","Tool call events and results injected into stream"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-bridge__cap_5","uri":"capability://safety.moderation.optional.api.key.based.authentication.and.access.control","name":"optional api key-based authentication and access control","description":"MCP-Bridge includes an authentication middleware layer (implemented in auth.py) that validates API keys on incoming requests before processing. The system supports optional API key authentication — when enabled, all requests must include a valid API key in the Authorization header. Authentication is configurable via environment variables, allowing operators to enable/disable it without code changes. The middleware intercepts requests early in the FastAPI pipeline, rejecting unauthorized requests before they reach downstream processing.","intents":["Protect MCP-Bridge endpoints from unauthorized access","Control which clients can invoke tools through the bridge","Implement per-client rate limiting or usage tracking via API keys","Secure deployments where the bridge is exposed to untrusted networks"],"best_for":["Production deployments exposed to external networks","Multi-tenant scenarios where different clients need different access levels","Organizations requiring audit trails of tool usage by client"],"limitations":["API key validation is simple string matching — no cryptographic verification","No built-in rate limiting or quota enforcement per API key","Keys are typically passed in plaintext in Authorization headers — requires HTTPS in production","No support for OAuth2, JWT, or other advanced authentication schemes"],"requires":["Python 3.9+","FastAPI middleware support","Environment variable or configuration file for API key storage","HTTPS in production to protect API keys in transit"],"input_types":["HTTP Authorization header with API key","Configuration specifying valid API keys"],"output_types":["401 Unauthorized response if authentication fails","Request allowed to proceed if authentication succeeds"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-bridge__cap_6","uri":"capability://automation.workflow.model.sampling.and.inference.server.selection","name":"model sampling and inference server selection","description":"MCP-Bridge includes a model sampling system that allows clients to specify which inference server or model to use for chat completions. The system forwards the model parameter from client requests to the downstream inference server, enabling selection between multiple models or inference backends. This allows a single bridge instance to route requests to different inference servers based on client preference, supporting scenarios where different models have different capabilities or performance characteristics.","intents":["Route requests to different inference servers based on model parameter","Support multiple models with different capabilities through a single bridge endpoint","Allow clients to select between fast/cheap and slow/capable models","Enable A/B testing of different inference backends"],"best_for":["Deployments with multiple inference servers or models available","Applications wanting to offer model selection to end users","Scenarios requiring different models for different use cases"],"limitations":["Model selection is pass-through — bridge doesn't validate that model exists on inference server","No built-in model registry or discovery — clients must know valid model names","No intelligent routing based on model capabilities or load","Inference server must support the model parameter in OpenAI API format"],"requires":["Python 3.9+","Downstream inference server supporting model parameter","Client knowledge of available model names"],"input_types":["OpenAI chat completion request with model parameter","Inference server configuration specifying available models"],"output_types":["Chat completion response from selected model/inference server"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-bridge__cap_7","uri":"capability://automation.workflow.mcp.server.configuration.and.lifecycle.management.via.environment.variables","name":"mcp server configuration and lifecycle management via environment variables","description":"MCP-Bridge reads MCP server configuration from environment variables at startup, allowing operators to specify which MCP servers to connect to, their transport type (stdio, HTTP, etc.), and any authentication credentials. The configuration is declarative and doesn't require code changes — adding or removing MCP servers is as simple as modifying environment variables and restarting the bridge. The system parses configuration at startup and establishes connections to all configured servers before accepting requests.","intents":["Configure which MCP servers the bridge connects to without code changes","Specify MCP server endpoints, transport types, and authentication","Enable different configurations for development, staging, and production","Simplify deployment by externalizing configuration"],"best_for":["DevOps teams managing bridge deployments across environments","Scenarios where MCP server configuration changes frequently","Docker/Kubernetes deployments using environment variables for configuration"],"limitations":["Configuration is read at startup only — changes require restart","No validation of configuration syntax — invalid config may cause startup failure","Environment variables can be verbose for complex configurations","No support for configuration files or dynamic configuration reloading"],"requires":["Python 3.9+","Environment variables set before bridge startup","Knowledge of MCP server endpoints and transport types"],"input_types":["Environment variables specifying MCP server configuration"],"output_types":["Established connections to configured MCP servers","Startup errors if configuration is invalid"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-bridge__cap_8","uri":"capability://automation.workflow.health.monitoring.and.connection.status.reporting","name":"health monitoring and connection status reporting","description":"MCP-Bridge includes health monitoring endpoints that report the status of connections to MCP servers and the bridge itself. The system periodically checks connectivity to MCP servers and exposes health status via HTTP endpoints, allowing deployment orchestration systems (Kubernetes, Docker Swarm, etc.) to detect failures and restart the bridge if needed. Health checks can be used for readiness probes (is the bridge ready to accept requests?) and liveness probes (is the bridge still running?).","intents":["Monitor the health of MCP server connections","Provide health status for Kubernetes readiness and liveness probes","Detect bridge failures and trigger automatic restarts","Report which MCP servers are currently connected and available"],"best_for":["Kubernetes deployments requiring health probes","Production environments needing automated failure detection","Monitoring systems that need to track bridge and MCP server status"],"limitations":["Health checks are basic connectivity tests — don't validate tool functionality","No historical health data — only current status is reported","Health check frequency is fixed — not configurable","No alerting or notification when health status changes"],"requires":["Python 3.9+","FastAPI health check endpoints","Orchestration system that can consume health check responses"],"input_types":["HTTP requests to health check endpoints"],"output_types":["JSON health status response (200 OK if healthy, 503 if unhealthy)","List of connected MCP servers and their status"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-bridge__cap_9","uri":"capability://automation.workflow.docker.containerization.and.deployment.support","name":"docker containerization and deployment support","description":"MCP-Bridge includes Docker support with a Dockerfile for containerization, enabling easy deployment in containerized environments. The Docker image bundles the bridge application with all dependencies, allowing operators to deploy via Docker Compose, Kubernetes, or other container orchestration platforms. The image is configured to read MCP server configuration from environment variables, making it suitable for multi-environment deployments.","intents":["Deploy MCP-Bridge in containerized environments (Docker, Kubernetes)","Simplify dependency management by bundling all requirements in a container image","Enable easy scaling and orchestration of bridge instances","Support CI/CD pipelines that build and deploy container images"],"best_for":["Teams using Docker or Kubernetes for infrastructure","Cloud deployments requiring containerized applications","CI/CD pipelines that build and push container images"],"limitations":["Requires Docker runtime — not suitable for serverless or non-containerized environments","Container image size may be large if dependencies are not optimized","No multi-stage build optimization in default Dockerfile","Requires knowledge of Docker and container orchestration"],"requires":["Docker 20.10+","Docker Compose or Kubernetes for orchestration","Container registry for storing images"],"input_types":["Dockerfile for building container image","Environment variables for runtime configuration"],"output_types":["Docker container image","Running container instance with bridge service"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","FastAPI 0.100+","OpenAI API-compatible inference server (local or remote)","One or more MCP servers configured and accessible","MCP server implementations accessible via stdio or HTTP transport","Configuration specifying MCP server endpoints (environment variables or config file)","Package manager (pip, Docker, etc.) for installing releases","Knowledge of semantic versioning for understanding compatibility","Knowledge of both OpenAI function-calling schema and MCP tool definition formats","Proper error handling for schema translation failures"],"failure_modes":["Adds one additional network hop and request transformation overhead (~50-100ms per request)","Requires downstream inference server to support OpenAI function-calling API format","Tool schema translation may lose MCP-specific metadata not representable in OpenAI schema","No built-in caching of tool definitions — refreshes on each request cycle","Connection pooling adds memory overhead proportional to number of MCP servers","Tool discovery happens at startup or on manual refresh — no hot-reloading of tool definitions","No built-in load balancing across multiple instances of the same MCP server","Reconnection backoff strategy is fixed — no configurable exponential backoff parameters exposed","Release frequency depends on maintainer availability","No long-term support (LTS) versions — all versions have same support duration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"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":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=mcp-bridge","compare_url":"https://unfragile.ai/compare?artifact=mcp-bridge"}},"signature":"rjC5GKbDcEoP8rA+Rm2ljoQuxMRF7UQcN0eKjONAA4V3mP0Qfo40x+8ieor0XeaG9v4cM24bux2FHKmMktHFAA==","signedAt":"2026-06-20T10:41:39.069Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-bridge","artifact":"https://unfragile.ai/mcp-bridge","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-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"}}