MCP Servers · SecretiveShell

MCP-Bridge

MCP-Bridge is a Python middleware that translates between OpenAI-compatible API calls and MCP (Model Context Protocol) tools, allowing any OpenAI API client to access MCP tool capabilities without native MCP support. It acts as a proxy layer, injecting MCP tool definitions into requests sent to an inference engine, then routing tool calls back to MCP servers.

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Source: GitHub — github.com/SecretiveShell/MCP-Bridge

928

GitHub stars

117

Forks

Python

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	SecretiveShell/MCP-Bridge
Owner	SecretiveShell
Primary language	Python
License	MIT — OSI-approved
Stars	928
Forks	117
Open issues	36
Latest release	0.5.1 (2025-02-08)
Last updated	2025-12-08
Source	https://github.com/SecretiveShell/MCP-Bridge

What MCP-Bridge is

MCP-Bridge implements an OpenAI-compatible REST API (via FastAPI) that intercepts chat/completion requests, enumerates MCP tool definitions from configured MCP servers, forwards augmented requests to a local inference engine (vLLM, Ollama), manages tool invocations via MCP protocol, and returns structured responses. It also exposes raw MCP primitives (tools, sampling, resources) via REST endpoints and provides an SSE bridge for MCP clients.

Quickstart

Get the MCP-Bridge source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/SecretiveShell/MCP-Bridge.git2cd MCP-Bridge3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Bridging Legacy OpenAI Clients to MCP Tools

Organizations with existing tools, applications, or platforms (e.g., Open WebUI, custom chatbots) that expect OpenAI API compatibility can use MCP-Bridge to access MCP tool ecosystems without rewriting client code. Ideal for teams with standardized on OpenAI API patterns who want MCP capability without full protocol migration.

Local Inference with Tool Orchestration

Teams running self-hosted inference engines (vLLM, Ollama) that need lightweight tool routing and orchestration can deploy MCP-Bridge as a thin orchestration layer. Useful for privacy-sensitive workloads or cost-optimized setups where avoiding cloud LLM APIs is a requirement.

Rapid MCP Tool Integration Testing

Developers building or vetting MCP servers and tools can use MCP-Bridge to test tool definitions and invocations without implementing full MCP protocol handling. The REST API and CLI testing via `mcp-cli` enable quick validation of tool behavior.

Implementation considerations

Requires an inference engine with tool-call support (vLLM or Ollama confirmed compatible; others unknown). Verify inference engine model and capability alignment before deployment.
Configuration via JSON file (file mount, HTTP URL, or env var) is required. Plan configuration management strategy, especially for API key security and multi-MCP-server setups.
Tool execution happens synchronously in request/response cycle. Monitor inference engine performance under concurrent tool calls; streaming completions are not yet supported, limiting throughput for high-traffic scenarios.
MCP server lifecycle management (process spawning, health checks, restarts) is handled by MCP-Bridge but error handling and recovery patterns are not documented. Plan operational monitoring.
Ensure inference engine and MCP servers are network-accessible from MCP-Bridge container/process. Document network topology and firewall rules early.

When to avoid it — and what to weigh

Requiring Production-Grade Enterprise Support — Project is seeking new maintainers and explicitly states it is 'soft deprecated' in favor of Open WebUI's native MCP support (v0.6.31+). Not recommended for mission-critical systems needing guaranteed SLA or vendor-backed support.
Native Cloud LLM API Direct Tooling (OpenAI, Anthropic) — If your application can directly call OpenAI or Anthropic APIs with native tool support, MCP-Bridge adds unnecessary intermediary complexity. Consider direct API integration or native MCP support in your platform instead.
Streaming Completions Requirement — Streaming completions are not implemented. If your use case requires real-time token streaming (e.g., live chat interfaces), this tool is incomplete for that workload.
Complex Resource-Heavy Tool Chains — MCP resources are listed as 'planned' and not yet supported. If your MCP servers rely heavily on resource retrieval (file handling, large context), current implementation will not support this.

License & commercial use

MIT License. Permissive, OSI-approved. Allows commercial use, modification, and redistribution with attribution and no warranty.

MIT is a permissive OSI license that permits commercial use. However, project is seeking new maintainers and is 'soft deprecated' in favor of Open WebUI native MCP support. Commercial users should evaluate long-term maintenance risk and consider native platform integration as an alternative.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Moderate
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

Optional API key authentication supported via Bearer token in config.json. No encryption, TLS, or rate-limiting documented. Inference engine communication and MCP server invocation security depends on network isolation and inference engine configuration. No security audit or vulnerability disclosure process mentioned. Recommended: run behind reverse proxy with TLS, restrict network access, rotate API keys regularly, audit MCP server sources.

Alternatives to consider

Open WebUI v0.6.31+ (Native MCP)

Project explicitly recommends this as the primary alternative. Open WebUI natively supports MCP without a bridge layer, eliminating the intermediary and associated maintenance burden. Preferred if you control the client.

Anthropic Claude API + Native MCP

If building on Claude/Anthropic, native MCP support in Claude for Desktop and Claude API may eliminate need for a bridge. Direct integration avoids intermediary latency and maintenance risk.

LangChain / LlamaIndex Tool Frameworks

General-purpose Python LLM tool orchestration libraries that support both OpenAI and local models with tool calling. Better maintained and broader ecosystem, but require client rewrite and don't act as an OpenAI API proxy.

Software development agency

Build on MCP-Bridge with DEV.co software developers

MCP-Bridge is suitable for teams bridging OpenAI API clients to MCP tool ecosystems or running local inference with tool orchestration. However, it is seeking maintainers and soft-deprecated in favor of Open WebUI native support. We recommend evaluating Open WebUI or native Anthropic MCP integration first, then assessing this tool for legacy client bridging or short-term deployments.

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Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

mcp-proxy mysql_mcp_server langchain-mcp-adapters

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MCP-Bridge FAQ

Is MCP-Bridge actively maintained?

Project is actively seeking new maintainers and is marked 'soft deprecated' in favor of Open WebUI native MCP support (v0.6.31+). Recent commits suggest moderate activity, but long-term maintenance is uncertain. Not recommended for production systems requiring guaranteed support.

Does MCP-Bridge support streaming responses?

No. Streaming chat completions are working, but streaming completions (non-chat) are not implemented. If your use case requires token-by-token streaming, this is a gap.

What inference engines are supported?

vLLM is confirmed to work. Ollama is listed as 'should be compatible' but not explicitly tested. Any inference engine exposing OpenAI-compatible chat/completion endpoints with tool-call support is a candidate; compatibility is unknown for others.

Can I use MCP-Bridge with Claude or OpenAI API directly?

No. MCP-Bridge is a proxy *to* a local inference engine, not a bridge *from* Claude or OpenAI API. You must run a local inference engine (vLLM, Ollama, etc.) and configure MCP-Bridge to point to it.

Software development & web development with DEV.co

Adopting MCP-Bridge is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate mcp servers software in production.

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