mcplex-backplane 0.3.0

Stateless HTTP proxy that exposes any REST API as an MCP tool

MCPlex

A stateless HTTP proxy that exposes any REST API as an MCP tool — no SDK, no wrapper library, no per-connector code.

MCPlex sits between AI coding agents (Claude Code, Cursor, Codex) and your backend REST APIs. Agents discover tools via the standard MCP tools/list handshake and call them via tools/call. MCPlex translates those calls into HTTP requests to your backend.

The key idea: connectors are YAML, not Python. Adding a new tool means adding a few lines of config — method, URL path, parameter mapping. The generic HTTP proxy handler does the rest.

What It's Meant to Do

Engineering teams build internal AI tools — incident responders, CI diagnosers, policy checkers, DORA metric dashboards. Each tool ships with its own UI or CLI. Adoption is low because engineers won't learn N different interfaces.

MCPlex changes the distribution model: one MCP server, all tools. Agents discover everything on startup. Engineers never leave their editor.

The repo includes 4 public connectors (ai-code-guardian, ci-doctor, sprint-intelligence, ai-incident-commander) as a concrete demonstration — 9 tools across 4 systems, all exposed through a single /mcp endpoint.

What It Is Not

• Not an AI framework. MCPlex contains zero LLM calls, zero prompts, zero model dependencies. It is a pure HTTP proxy.
• Not an MCP SDK. MCPlex implements the MCP wire protocol directly. Backend tools never import an MCP library or add an MCP dependency.
• Not a replacement for backend APIs. MCPlex does not transform data or add business logic. It proxies calls to your existing REST endpoints.

Architecture

                    config.yaml
                         │
              ┌──────────▼──────────┐
              │  Tool Registry      │
              │  name → route       │
              └──────────┬──────────┘
                         │
              ┌──────────▼──────────┐
              │  HTTP Proxy Handler │
              │  (generic — reads   │
              │   method, path,     │
              │   param_mapping     │
              │   from config)      │
              └────┬────┬────┬──────┘
                   │    │    │
             ┌─────▼┐ ┌▼───┐┌▼────┐
             │ API A│ │API B││API C│
             └──────┘ └────┘└─────┘

1. On startup, MCPlex reads config.yaml and builds a tool registry.
2. Each type: http connector generates an async proxy handler.
3. An agent connects and calls tools/list — MCPlex returns the catalog.
4. The agent calls tools/call — MCPlex maps MCP parameters to HTTP parameters and makes the request.
5. The backend response is returned as an MCP tool result.

Two transport modes, auto-detected by the Accept header:

Accept header	Response format
application/json	JSON-RPC over HTTP POST
text/event-stream	Server-Sent Events (Streamable HTTP)

Quick Start

pip install mcplex

# Copy and edit environment (for docker-compose)
cp .env.example .env

# Start with default config
mcplex serve --config config.yaml

Connect Claude Code: claude --mcp http://localhost:8000/mcp

Included Connectors

4 MIT-licensed public repos, wired as MCP tools via thin API adapters (see Integration Guide for the pattern):

Connector	Repo	Tools
guardian	ai-code-guardian	guardian_check_policy, guardian_get_coverage
ci-agent	ci-doctor	ci_diagnose_failure, ci_get_pipeline_history
sprintsense	sprint-intelligence	dora_get_metrics, dora_get_trend
incident-cmdr	ai-incident-commander	incident_query_active, incident_query_history, incident_get_timeline

Total: 9 MCP tools across 4 AI systems (3 real repos + 1 test-bench mock).

Docs

Doc	Description
Integration Guide	How to connect repos via MCP API adapters
Specification	Architecture, transport, connector design
PRD	Product requirements and user stories
Sprint Plan (WBS)	Work breakdown and epics
Config Reference	YAML schema for connectors
E2E Test Plan & Results	Test plan, 9/9 results, 15 screenshots
Code Review	Internal code review

Roadmap

Shipped (v0.3.0)

• Generic HTTP proxy connector (YAML-driven)
• Streamable HTTP + SSE auto-detect
• 4 public repo integrations
• Docker compose deployment
• 41 unit tests, 9 E2E tests passing

Next

• OSS readiness: issue templates, CODE_OF_CONDUCT, SECURITY.md
• GitHub Actions CI
• Contributing guide
• Claude Code demo recording

Future (v1.0.0)

• OAuth 2.0 / OIDC authorization
• Write tool approval flow
• Unified audit logging
• PyPI publish
• Rate limiting and config hot-reload

License

MIT