๐๏ธ Production-Grade MCP Server + Agentic System
A reference implementation of an MCP server designed to actually ship
Multi-tenant ยท Authenticated ยท Observable ยท Rate-limited ยท Cached ยท Circuit-broken ยท Governed
๐ Full Step-by-Step Blog Walkthrough
This repository is the companion codebase for a long-form blog post that walks through every single component end to end, with every line of code explained in context. Start there if you want to understand the "why" behind the architecture before reading the code.
๐ Building a Production-Grade MCP Server Architecture with Agentic System โ
๐ฏ What This Is
Most MCP tutorials end with a @tool decorator that returns "hello world". That is fine for a demo. It is not what ships.
This repository is a reference implementation of an MCP server designed to run in production: multi-tenant, authenticated, observable, rate-limited, cached, circuit-broken, and governed. It exposes a company's heterogeneous data layer (Postgres, Elasticsearch, S3, vector DB) to AI agents as a single, secure tool surface, and ships with a four-agent support copilot (Planner โ Retriever โ Synthesizer โ Critic) that uses it end to end.
The codebase is deliberately organised around twelve components that keep showing up on the 3 AM pager when teams skip them. Each one lives in its own module and can be read, replaced, or extended independently.
๐๏ธ Architecture Overview
The complete production-grade system: MCP server dispatch pipeline on the right, four-agent orchestrator on the left, data plane on top, observability on the bottom, identity and governance as crosscutting concerns.
๐งฉ The 12 Components
๐ Diving Deeper, Section by Section
Each diagram below links back to the corresponding section in the blog, where every line of code is walked through in detail.
๐ฆ Data Persistence Layer
Postgres + Row-Level Security ยท Tenant isolation at the DB layer
๐ช Transport & Session Layer
Dual transport ยท Stateless session ยท Middleware chain
๐ Authentication, Policy & Governance
OAuth 2.1 ยท YAML policies ยท Human-in-the-loop approvals
๐ง Tool Execution Engine
Three-level hierarchy ยท Atomic ยท Composed ยท Workflow
๐ Reliability Layer
Circuit breakers ยท Retry with jitter ยท ATBA budget allocator
โก Rate Limiting & Caching
Redis token bucket ยท Two-tier cache ยท Stampede lock
๐ญ Observability Stack
OpenTelemetry ยท Prometheus ยท Audit logs ยท One trace ID
๐ค Multi-Agentic Architecture
Four-agent design ยท Planner ยท Retriever ยท Synthesizer ยท Critic
๐ผ The Orchestrator Flow
End-to-end agent orchestration with one bounded revise loop
๐ Quick Start
Prerequisites
- Docker & Docker Compose
- Python 3.11+ (only for running the CLI locally)
- An Anthropic API key (for the agent layer)
1. Clone and Configure
git clone https://github.com/FareedKhan-dev/production-grade-mcp-agentic-system.git
cd production-grade-mcp-agentic-system
cp .env.example .env
Edit .env and set at minimum:
ANTHROPIC_API_KEYโ for the agent layerATLAS_AUTH_JWKS_URLโ your OAuth 2.1 provider's JWKS endpoint (or leave default for dev)
2. Bring Up the Stack
docker compose up -d
That brings up the full local environment:
3. Run the Support Copilot CLI
pip install -e .
export ATLAS_MCP_URL=http://localhost:8080
export ATLAS_MCP_TOKEN=dev-token
export ATLAS_TENANT=acme
export ANTHROPIC_API_KEY=sk-ant-...
atlas-copilot "Why was the refund on order o_9002 for CUST-1001 delayed?"
You will see the four agents run end-to-end, the final draft printed with [S1][S2] citations, and a full trace summary including token counts, tool calls, and the run_id that ties back to Jaeger.
4. Connect from Claude Desktop / Cursor
Add this to your MCP host config:
{
"mcpServers": {
"production-mcp": {
"type": "http",
"url": "http://localhost:8080/mcp",
"headers": {
"Authorization": "Bearer ${ATLAS_MCP_TOKEN}",
"X-Tenant-Id": "acme"
}
}
}
}
๐ Repository Structure
.
โโโ ๐ README.md
โโโ ๐ณ docker-compose.yml # Full local stack: app + data + observability
โโโ ๐ณ Dockerfile # Two-stage build, non-root runtime
โโโ ๐ LICENSE
โโโ ๐ฆ pyproject.toml # Dependencies, dev tools, CLI entry points
โโโ โ๏ธ .env.example # Every setting documented by component
โ
โโโ ๐ง config/ # Runtime configuration (hot-reloadable)
โ โโโ http_allowlist.yaml # Per-tenant outbound HTTP allowlist
โ โโโ policy.yaml # YAML-driven authorization policies
โ
โโโ ๐ข deploy/ # Deployment sidecar configs
โ โโโ otel/config.yaml # OpenTelemetry Collector pipeline
โ โโโ prometheus/prometheus.yml # Prometheus scrape targets
โ โโโ sql/init.sql # Schema + RLS policies + seed data
โ
โโโ ๐ docs/ # Deep-dive documentation
โ โโโ AGENT_SYSTEM.md # Multi-agent orchestrator internals
โ โโโ ARCHITECTURE.md # The 12 components in detail
โ โโโ DEPLOYMENT.md # K8s, Cloudflare Workers, bare-metal
โ
โโโ ๐ง src/atlas_mcp/ # Main application source
โ โโโ config.py # Centralized typed settings
โ โโโ server.py # โก Component 1: Transport & dispatch
โ โ
โ โโโ ๐ค agents/ # Four-agent support copilot
โ โ โโโ planner.py # Emits retrieval plan JSON
โ โ โโโ retriever.py # Bounded tool-calling loop
โ โ โโโ synthesizer.py # Drafts reply with citations
โ โ โโโ critic.py # Approves or sends one revise
โ โ โโโ orchestrator.py # Wires the four agents together
โ โ โโโ mcp_client.py # Thin JSON-RPC MCP client
โ โ โโโ memory.py # STM (Redis) + LTM (vector)
โ โ โโโ cli.py # atlas-copilot CLI entry point
โ โ
โ โโโ ๐ auth/ # Components 2 + 3
โ โ โโโ oauth.py # JWT + JWKS validation
โ โ โโโ middleware.py # Bearer token extraction
โ โ โโโ policy.py # YAML-driven policy engine
โ โ
โ โโโ ๐ก๏ธ governance/ # Component 12
โ โ โโโ tenant.py # Tenant pinning middleware
โ โ โโโ approval.py # Human-in-the-loop gate
โ โ
โ โโโ ๐ง tools/ # Components 4 + 6
โ โ โโโ registry.py # In-memory tool index + discovery
โ โ โโโ base.py # Tool abstract base + metadata
โ โ โโโ atomic/ # Level 1: one backend each
โ โ โโโ composed/ # Level 2: deterministic chains
โ โ โโโ workflow/ # Level 3: multi-step procedures
โ โ
โ โโโ ๐ reliability/ # Component 7
โ โ โโโ circuit_breaker.py # 3-state machine per tool
โ โ โโโ retry.py # Exponential backoff + jitter
โ โ โโโ atba.py # Adaptive Timeout Budget Allocation
โ โ
โ โโโ ๐ฆ ratelimit/ # Component 8
โ โ โโโ limiter.py # Redis token bucket (Lua-atomic)
โ โ
โ โโโ โก cache/ # Component 9
โ โ โโโ manager.py # L1 + L2 cache with stampede lock
โ โ
โ โโโ ๐งฑ errors/ # Component 10
โ โ โโโ framework.py # Structured Error Recovery (SERF)
โ โ
โ โโโ ๐ญ observability/ # Component 11
โ โ โโโ tracing.py # OpenTelemetry spans
โ โ โโโ metrics.py # Prometheus instruments
โ โ โโโ audit.py # Structured JSONL audit log
โ โ
โ โโโ โ
validation/ # Component 5
โ โโโ schemas.py # Tool call envelope
โ
โโโ ๐งช tests/ # Narrow tests, load-bearing properties
โโโ test_circuit_breaker.py # State machine transitions
โโโ test_errors.py # SERF wire format + retry semantics
โโโ test_policy.py # Deny-beats-allow + default-deny
๐จ Tech Stack
๐งช Testing
The test suite is deliberately narrow, covering the three load-bearing safety properties:
pip install -e ".[dev]"
pytest -v
test_circuit_breaker.pyโ state machine transitions, retryable vs deterministic error classificationtest_errors.pyโ SERF wire format, retry semantics, MCP-level error datatest_policy.pyโ default-deny, deny-beats-allow, glob matching, PII condition blocking
๐ฃ๏ธ Production Deployment
For running this in an actual production environment (managed Postgres, real OAuth provider, SIEM integration, Kubernetes), see docs/DEPLOYMENT.md.
Key swaps between local dev and production:
๐ Documentation
- ๐ Blog Walkthrough โ Building a Production-Grade MCP Server (recommended starting point)
- ๐๏ธ
docs/ARCHITECTURE.mdโ The 12 components in depth - ๐ค
docs/AGENT_SYSTEM.mdโ Multi-agent orchestrator internals - ๐ข
docs/DEPLOYMENT.mdโ Production deployment options
๐ License
MIT. See LICENSE.
โญ If this helped you, please consider starring the repo
Built with โ and a lot of 3 AM debugging
๐ Read the full blog walkthrough ยท ๐ Report an issue ยท ๐ฌ Start a discussion