Which integrations are available for this server?

Exposes a Prometheus-compatible /metrics endpoint to enable monitoring and tracking of server performance and tool invocation metrics.

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@QMCP list all available tools and recent invocation history" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

QMCP - Model Context Protocol Server

A spec-aligned Model Context Protocol (MCP) server built with FastAPI.

Features

✅ Tool Discovery - List available tools via /v1/tools
✅ Tool Invocation - Execute tools via /v1/tools/{name}
✅ Invocation History - Audit trail via /v1/invocations
✅ Human-in-the-Loop - Request human input via /v1/human/*
✅ Persistence - SQLite with SQLModel/aiosqlite
✅ Python Client - qmcp.client.MCPClient for workflows
✅ Metaflow Examples - Ready-to-use flow templates
✅ Agent Framework - SQLModel schemas + mixins for agent types/topologies
✅ PydanticAI Integration - Create agents from QMCP models with full audit trail
✅ Structured Logging - JSON logs with structlog
✅ Request Tracing - Correlation IDs across requests
✅ Metrics - Prometheus-compatible /metrics endpoint
✅ CLI Interface - Manage via qmcp command

Quick Start

# Install dependencies uv sync # Start the server uv run qmcp serve # Or with development reload uv run qmcp serve --reload

See quickstart.md for a copy-paste walkthrough.

Adoption and Onboarding

Adoption checklist:

Decide how the server is hosted (local, container, or VM) and who can reach it.
Set QMCP_HOST, QMCP_PORT, and QMCP_DATABASE_URL for your environment.
Standardize X-Correlation-ID values for audit trails across clients.
Decide how humans submit HITL responses (UI or API).
Wire /metrics into your monitoring stack.

Onboarding path:

uv sync --all-extras
Run the end-to-end tutorial below.
uv run qmcp serve for local exploration.

End-to-End Tutorial (HITL approval workflow)

This tutorial mirrors the end-to-end test tests/test_hitl.py::TestHITLWorkflow::test_complete_approval_workflow.

Copy and paste:

uv sync --all-extras uv run pytest tests/test_hitl.py::TestHITLWorkflow::test_complete_approval_workflow -v

Client Library

from qmcp.client import MCPClient with MCPClient(base_url="http://localhost:3333") as client: # List tools tools = client.list_tools() # Invoke a tool result = client.invoke_tool("echo", {"message": "Hello!"}) print(result.result) # Human-in-the-loop request = client.create_human_request( request_id="approval-001", request_type="approval", prompt="Approve deployment?", options=["approve", "reject"] ) response = client.wait_for_response("approval-001", timeout=3600)

See docs/client.md for full API documentation.

CLI Commands

# Start the server qmcp serve [--host HOST] [--port PORT] [--reload] # Start the server for Docker-based flows qmcp cookbook serve [--host 0.0.0.0] [--port PORT] [--reload] # List registered tools qmcp tools list # Show configuration qmcp info # Run a cookbook flow in Docker qmcp cookbook simple-plan --goal "Deploy a web service" # Start the server + run a cookbook flow (unified dev) qmcp cookbook dev simple-plan --goal "Deploy a web service" # Run a cookbook flow via the generic runner qmcp cookbook run simple-plan --goal "Deploy a web service" # Run other cookbook recipes (flow args are passed through) qmcp cookbook run approved-deploy --service "api-gateway" --environment "staging" qmcp cookbook dev local-qc-gauntlet --change-summary "Add audit fields" --target-area "metrics, logging" # Run a cookbook flow in Docker explicitly qmcp cookbook docker simple-plan --goal "Deploy a web service" # If the qmcp shim cannot be installed (Windows) uv run --no-sync python -m qmcp cookbook run simple-plan --goal "Deploy a web service" # Run tests with auto setup/teardown qmcp test [-v] [--coverage] [TEST_PATH]

Cookbook flows run in Docker and require Docker Desktop (Linux engine). Add --no-sync to skip syncing flow dependencies if the image is already built.

API Endpoints

Endpoint	Method	Description
`/health`	GET	Health check
`/v1/tools`	GET	List available tools
`/v1/tools/{name}`	POST	Invoke a tool
`/v1/invocations`	GET	List invocation history
`/v1/invocations/{id}`	GET	Get single invocation
`/v1/human/requests`	POST	Create human request
`/v1/human/requests`	GET	List human requests
`/v1/human/requests/{id}`	GET	Get request with response
`/v1/human/responses`	POST	Submit human response
`/metrics`	GET	Prometheus metrics
`/metrics/json`	GET	Metrics as JSON

Built-in Tools

echo - Echo input back (for testing)
planner - Create execution plans
executor - Execute approved plans
reviewer - Review and assess results

Development

# Install dev dependencies uv sync --all-extras # Run tests (with auto cleanup) uv run qmcp test -v # Run tests with coverage uv run qmcp test --coverage # Run linter uv run ruff check .

Architecture

See docs/architecture.md for the full architectural overview.

The system follows a three-plane architecture:

Client/Orchestration - Metaflow workflows (MCP client)
MCP Server - FastAPI service (this project)
Execution/Storage - Tools and database

Documentation

Quickstart - Copy-paste setup and validation
Overview - What and why
Architecture - How and constraints
Tools - Tool capabilities
Client Library - Python client API
Human-in-the-Loop - HITL guide
Agent Framework - Agent schemas and mixins
PydanticAI Integration - Agent runtime integration
Deployment - Production deployment guide
Contributing - Development guidelines
Roadmap - Development phases

Example Flows

See examples/flows/ for Metaflow integration examples:

simple_plan.py - Basic tool invocation
approved_deploy.py - HITL approval workflow
local_agent_chain.py - Local LLM plan -> review -> refine with SQLModel artifacts
local_qc_gauntlet.py - Local LLM QC checklist/task/gate builder
local_release_notes.py - Local LLM release notes and doc update suggestions

For local LLM flows, install extras with uv sync --extra flows. Start uv run qmcp serve --host 0.0.0.0 when --use-mcp True to enable MCP calls from Docker-based flows. On Windows, prefer running flows in a Linux container to avoid platform-specific Metaflow dependencies.

Docker runner (recommended on Windows):

docker compose -f docker-compose.flows.yml build docker compose -f docker-compose.flows.yml run --rm flow-runner \ examples/flows/local_agent_chain.py run --use-mcp True --goal "..."

Set MCP_URL and LLM_BASE_URL (or pass --mcp-url / --llm-base-url) when running in Docker, e.g. http://host.docker.internal:3333.

License

MIT

QMCP