Install
$ agentstack add mcp-deghosal-2026-mcplex ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ● Network access Used
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ● Environment & secrets Used
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
MCPlex
[](.github/workflows/ci.yml) [](LICENSE) [](https://www.python.org/) [](https://pypi.org/project/mcplex/) [](https://github.com/astral-sh/ruff) [](https://github.com/python/mypy) [](CODEOFCONDUCT.md) [](https://www.bestpractices.dev/projects/13801)
> Early proof-of-concept: a YAML-configured MCP proxy that maps simple JSON REST endpoints (GET + query params, POST + JSON body) into MCP tools. Not production-hardened — see [Roadmap](#roadmap).
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. Zero LLM logic, zero model dependencies, ~400 lines of Python.
What It's Meant to Do
Most engineering teams have a graveyard of internal tools built before AI coding agents existed — CLIs, dashboards, webhook receivers, internal REST APIs. These tools work. They have users, auth, governance, and business logic baked in. What they don't have is an interface that AI agents can call.
MCPlex gives those tools a 2nd life. You don't rewrite them. You add a thin REST adapter if they don't already expose JSON (about 40 lines), point MCPlex at it with a YAML connector, and now any MCP-compatible agent can call them. The tool keeps its auth, its logic, its governance. MCPlex is just the new front door.
The key idea: connectors are YAML, not Python (for the 80% case where the backend already speaks JSON REST). Adding a new HTTP tool means adding a few lines of config — method, URL path, parameter mapping. The generic HTTP proxy handler does the rest.
The repo includes a 4-connector demo config as an illustration of the pattern — 3 connectors proxy to real sibling repos; the 4th runs on mock data via a native Python connector for backends without an HTTP server.
Current Limitations (v0.4.0)
| What works | What's planned | |---|---| | GET with query params + POST with JSON body | PUT / DELETE support | | Flat param_mapping (MCP arg → HTTP param) | Path-param templating (/api/{id}) | | JSON + non-JSON response handling (HTML/text wrapped) | | | Static header injection with ${ENV_VAR} interpolation | OAuth 2.0 / OIDC | | SSE heartbeat keepalive | Progress streaming, session continuity | | Identity propagation (X-MCP-Client-Name / Version headers) | | | Schema/argument validation (type, required, enum, min/max) | | | Rate limiting per-tool/per-agent | | | Structured audit logging (sessionid, userid, backend_url, etc.) | | | Connection pooling (shared httpx.AsyncClient) | | | 75 unit + integration tests | Write-tool approval flow | | | Config hot-reload |
What It Is Not
- Not an AI framework. MCPlex contains zero LLM calls, zero prompts,
zero model dependencies. It is a pure HTTP proxy.
- Not an MCP SDK. MCPlex implements the MCP wire protocol directly.
Backend tools never import an MCP library or add an MCP dependency.
- Not a replacement for backend APIs. MCPlex does not transform data
or add business logic. It proxies calls to your existing REST endpoints.
Architecture
config.yaml
│
┌──────────▼──────────┐
│ Tool Registry │
│ name → route │
└──────────┬──────────┘
│
┌──────────▼──────────┐
│ HTTP Proxy Handler │
│ (generic — reads │
│ method, path, │
│ param_mapping │
│ from config) │
└────┬────┬────┬──────┘
│ │ │
┌─────▼┐ ┌▼───┐┌▼────┐
│ API A│ │API B││API C│
└──────┘ └────┘└─────┘
- On startup, MCPlex reads
config.yaml, interpolates${ENV_VAR}patterns,
and builds a tool registry.
- Each
type: httpconnector generates an async proxy handler. - An agent connects and calls
tools/list— MCPlex returns the catalog. - The agent calls
tools/call— MCPlex validates arguments against the
declared JSON Schema, maps MCP parameters to HTTP parameters, and makes the request.
- The backend response is returned as an MCP tool result, with
isError
set when the tool fails.
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 (one-shot framing) |
What Is In This Repo
- MCPlex proxy server (Starlette, JSON-RPC, SSE transport)
- YAML config system (Pydantic models, env-var interpolation, config validation)
- Generic HTTP proxy handler (GET/POST with param mapping)
- Native Python connector fallback (
incidentgpt.py) for backends without an HTTP server - Test bench (mock server simulating all 4 backends)
- 50 unit tests, CI (lint, typecheck, test)
What Is NOT In This Repo (Sibling Repos)
The 4 demo connectors proxy to sibling repos. These must be cloned alongside mcplex for the full Docker Compose stack (see docker-compose.yml):
| Connector | Sibling Repo | Status | |-----------|-------------|--------| | guardian | ai-code-guardian | Real backend with MCP API adapter | | ci-agent | ci-doctor | Real backend with MCP API adapter | | sprintsense | sprint-intelligence | Real backend with MCP API adapter | | incident-commander | ai-incident-commander | CLI-only; uses test-bench mock via incidentgpt.py native connector |
Total: 9 MCP tools across 4 demo connectors (3 real repos + 1 test-bench mock).
To try it without cloning sibling repos, run the test bench: python tests/test_bench.py.
Quick Start
pip install mcplex-backplane
# Try with the test-bench mock (no sibling repos needed)
python tests/test_bench.py &
mcplex serve --config config.yaml
# Or use docker-compose for the full stack
cp .env.example .env
docker compose up
Connect Claude Code: claude --mcp http://localhost:8000/mcp
Included Connectors
The demo config.yaml includes 4 connectors — 3 proxy to real sibling repos, 1 runs on the test-bench mock:
| 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-commander | ai-incident-commander | incident_query_active, incident_query_history, incident_get_timeline |
Docs
| Doc | Description | |-----|-------------| | [Architecture](docs/architecture.md) | System design, data flow, design decisions | | [Deployment Guide](docs/deployment.md) | Production deployment, nginx config, security | | [Troubleshooting](docs/troubleshooting.md) | Common errors and resolution steps | | [Integration Guide](docs/integration/README.md) | How to connect repos via MCP API adapters | | [Specification](docs/spec/mcplex.md) | Architecture, transport, connector design | | [PRD](docs/prd/mcplex.md) | Product requirements and user stories | | [Sprint Plan (WBS)](docs/wbs/mcplex.md) | Work breakdown and epics | | [Config Reference](docs/config-reference.md) | YAML schema for connectors | | [E2E Test Plan & Results](tests/e2e/README.md) | Test plan, 9/9 results, 15 screenshots | | [Code Review](docs/review.md) | Internal code review |
Roadmap
Shipped (v0.3.0)
- Generic HTTP proxy connector (YAML-driven, GET/POST)
- JSON-RPC + one-shot SSE framing (auto-detect by Accept header)
- Config validation (required
base_url, env-var interpolation) isErrorflag on failed tool calls (MCP spec compliance)- JSON-RPC batch request handling
- 4-connector demo config
- OSS readiness: CI, issue templates, CODEOFCONDUCT, SECURITY.md, CONTRIBUTING.md
- 50 unit tests, Docker Compose deployment
Shipped (v0.4.0)
- Identity propagation (X-MCP-Client-Name / Version headers)
- Structured audit logging (sessionid, userid, backendurl, httpstatus)
- Per-tool / per-agent rate limiting
- Schema/argument validation (type, required, enum, min/max)
- Non-JSON response handling (HTML/text wrapped)
- SSE heartbeat keepalive
- Connection pooling (shared httpx.AsyncClient)
- 75 tests (unit + integration), self-contained mock-based integration tests
- PyPI publish as
mcplex-backplane - See [CHANGELOG.md](CHANGELOG.md) for full details
Future (v1.0.0)
- Write‑tool approval flow
- Config hot‑reload
- Path‑param templating (
/api/{id}) - PUT/DELETE support
- SSE progress streaming and session continuity
- OAuth 2.0 / OIDC
License
MIT
Resources
- Issues: github.com/deghosal-2026/mcplex/issues — bug reports and feature requests
- Security: [SECURITY.md](SECURITY.md) — report vulnerabilities privately
- Contributing: [CONTRIBUTING.md](CONTRIBUTING.md) — coding standards and PR process
- Changelog: [CHANGELOG.md](CHANGELOG.md) — release history
Source & license
This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: deghosal-2026
- Source: deghosal-2026/mcplex
- License: MIT
- Homepage: https://github.com/deghosal-2026/mcplex
Install and usage instructions live in the source repository linked above.
Reviews
No reviews yet, be the first.
Write a review
Versions
- v0.1.0 Imported from the upstream source.