MCP-Bridge
MCP ServerFree** 🐍 an openAI middleware proxy to use mcp in any existing openAI compatible client
Capabilities11 decomposed
openai api-compatible endpoint proxying with mcp tool injection
Medium confidenceMCP-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.
Implements transparent request/response transformation at the HTTP middleware layer using FastAPI, allowing unmodified OpenAI clients to access MCP tools by injecting tool schemas into the request before forwarding to inference servers, then extracting and routing tool calls back to MCP servers — no client-side changes required.
Unlike direct MCP client libraries that require application code changes, MCP-Bridge works with any existing OpenAI API client as a drop-in proxy, making it faster to integrate into legacy systems than rewriting client implementations.
dynamic mcp server connection management with multi-server support
Medium confidenceMCP-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.
Implements a centralized MCP Client Manager that maintains persistent connections to multiple MCP servers, aggregates their tool definitions into a unified registry, and handles connection lifecycle (reconnection, health checks) transparently — enabling a single bridge instance to serve tools from many MCP sources.
Compared to applications that connect directly to individual MCP servers, MCP-Bridge's multi-server aggregation allows a single proxy to unify tools from many sources, reducing client complexity and enabling centralized access control.
release process and version management
Medium confidenceMCP-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.
Implements semantic versioning and automated release process with published artifacts to package registries, enabling users to install and manage specific versions of MCP-Bridge with clear changelog documentation.
Compared to projects without formal release processes, MCP-Bridge's versioning and changelog provide clarity on changes and enable stable, reproducible deployments.
bidirectional tool schema translation between openai and mcp formats
Medium confidenceMCP-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.
Implements bidirectional schema translation at the tool definition level, converting between MCP and OpenAI formats while preserving semantic meaning — allowing tools defined in MCP format to be transparently used by OpenAI API clients without requiring tool authors to maintain dual definitions.
Unlike solutions that require tools to be defined separately for each protocol, MCP-Bridge's translation layer allows a single MCP tool definition to be used with OpenAI clients, reducing maintenance burden and ensuring consistency.
tool call routing and execution with mcp server dispatch
Medium confidenceWhen 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.
Implements a tool dispatch layer that maps tool_call objects to their source MCP servers and executes them synchronously within the request/response cycle, enabling agentic workflows where LLM tool calls are immediately executed and results fed back for further reasoning.
Unlike client-side tool execution where applications must implement their own routing logic, MCP-Bridge's centralized dispatch ensures consistent tool execution semantics and error handling across all clients.
streaming chat completion responses with server-sent events bridge
Medium confidenceMCP-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.
Implements a streaming response handler that manages both token streaming and mid-stream tool calls, buffering tool invocations until complete, executing them, and injecting results back into the token stream — enabling real-time streaming while maintaining tool-calling semantics.
Unlike simple streaming proxies that cannot handle tool calls, MCP-Bridge's SSE bridge manages the complexity of tool execution during streaming, allowing clients to receive real-time tokens while tools are being executed in the background.
optional api key-based authentication and access control
Medium confidenceMCP-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.
Implements optional API key-based authentication as a FastAPI middleware layer that validates requests early in the pipeline, allowing operators to enable/disable authentication via environment variables without code changes — providing basic access control for deployments.
While simpler than OAuth2 or JWT-based approaches, MCP-Bridge's API key authentication is sufficient for basic access control and can be deployed quickly without external authentication services.
model sampling and inference server selection
Medium confidenceMCP-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.
Implements model sampling as a pass-through parameter that allows clients to specify which inference server or model to use, enabling a single bridge instance to route requests to different backends based on client preference without requiring bridge-level model management.
Unlike load balancers that distribute requests blindly, MCP-Bridge's model sampling gives clients explicit control over which inference backend processes their request, enabling use cases like model selection and A/B testing.
mcp server configuration and lifecycle management via environment variables
Medium confidenceMCP-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.
Implements configuration management via environment variables that are read at startup, allowing operators to specify MCP server connections declaratively without code changes — enabling easy deployment across different environments.
Compared to hardcoded configuration or complex configuration files, environment variable-based configuration is simpler for containerized deployments and integrates naturally with Docker and Kubernetes.
health monitoring and connection status reporting
Medium confidenceMCP-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?).
Implements health monitoring endpoints that report bridge and MCP server connection status, enabling integration with Kubernetes health probes and other orchestration systems for automated failure detection and recovery.
Unlike simple HTTP health checks that only verify the bridge is running, MCP-Bridge's health monitoring also checks MCP server connectivity, providing visibility into the full system health.
docker containerization and deployment support
Medium confidenceMCP-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.
Provides a Dockerfile and Docker Compose configuration that bundles MCP-Bridge with all dependencies, enabling one-command deployment in containerized environments with environment variable-based configuration.
Compared to manual installation and configuration, Docker containerization simplifies deployment and ensures consistency across development, staging, and production environments.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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[](https://badge.fury.io/js/orval) [](https://opensource.org/licenses/MIT) [ needing tool support
- ✓Organizations building tool-calling features without rewriting client applications
- ✓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
- ✓Production deployments requiring stable, versioned releases
- ✓Teams wanting to track and manage bridge upgrades
Known 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
- ⚠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
Requirements
Input / Output
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** 🐍 an openAI middleware proxy to use mcp in any existing openAI compatible client
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