MCP Agent Toolkit

A production-pattern FastMCP server + LangGraph agent: a FastMCP server exposes genuinely working tools (doc search, record lookup, text analysis) with scoped permissions, and a LangGraph agent discovers and calls them over MCP — traced with LangSmith, served over FastAPI, deployable via Docker/Helm/Argo CD.

Honesty notes: all bundled docs and records are synthetic demo data. The service runs in mock mode by default (deterministic fake LLM, no keys, no cost) and is a reference implementation — not deployed to production, no business metrics claimed. Live AWS Bedrock calls are supported but were not exercised here (no AWS credentials in this environment).

Architecture

flowchart TD
    A[POST /ask] --> B[decide: LLM picks a tool or answers]
    B -->|tool call| C[MCP client: scoped permission check]
    C -->|allowed| D[FastMCP server: doc_search / record_lookup / text_stats]
    C -->|denied| H[PermissionError → recorded observation]
    D --> E[observation appended to state]
    H --> E
    E --> B
    B -->|final answer| V{validate: output checks}
    V -->|pass| F[return answer + tools_used]
    V -->|fail| S[safe fallback answer]

Components

• mcp_toolkit/server.py — FastMCP server with 3 real tools (all read-only): doc_search (BM25 over bundled docs), record_lookup (by ID), text_stats (counts, reading time, keywords). Runnable standalone: python -m mcp_toolkit.server (streamable HTTP on :8100).

• mcp_toolkit/mcp_client.py — MCP client wrapper with scoped permissions: calls outside allowed_tools raise PermissionError. Connects in-process (tests/dev) or over HTTP via MCP_SERVER_URL.

• mcp_toolkit/agent.py — LangGraph loop (decide → act → decide … → validate), max 4 tool steps.

• mcp_toolkit/api.py — FastAPI: POST /ask, GET /tools (live MCP tool discovery), GET /health.

Observability: LangSmith tracing auto-enabled when LANGSMITH_API_KEY is present; degrades gracefully otherwise.

Secrets: AWS Secrets Manager (mcp-agent-toolkit/config JSON) first, env vars as fallback. No secrets in code, tests, or history.

Related MCP server: LangGraph FastAPI MCP Server

Measured results

Measured locally on 2026-09-23 (mock LLM, in-process MCP, synthetic bundled data):

Setup

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

Mock mode (default):

MOCK_MODE=true python -m uvicorn mcp_toolkit.api:app --port 8000

Standalone MCP server (for external MCP clients):

MOCK_MODE=true python -m mcp_toolkit.server   # http://0.0.0.0:8100

Live Bedrock mode: set MOCK_MODE=false, AWS_REGION, BEDROCK_MODEL_ID; provide keys via Secrets Manager or env.

Usage

curl -X POST localhost:8000/ask -H 'Content-Type: application/json' \
  -d '{"question": "What is the refund policy?"}'
# {"answer": "Based on tool results — doc_search: [...]", "tools_used": ["doc_search"], "steps": 1, ...}

curl -X POST localhost:8000/ask -H 'Content-Type: application/json' \
  -d '{"question": "What does the refund policy say, and what is the status of record R-2003?"}'
# multi-hop: tools_used: ["doc_search", "record_lookup"]

curl localhost:8000/tools   # live MCP tool discovery

Scoped permissions:

MCP_ALLOWED_TOOLS=doc_search python -m uvicorn mcp_toolkit.api:app --port 8000
# record_lookup / text_stats calls now raise PermissionError inside the agent

API reference

Deployment

Docker (multi-stage, non-root):

docker build -t ghcr.io/saimudunuri04/mcp-agent-toolkit:latest .
docker run -p 8000:8000 ghcr.io/saimudunuri04/mcp-agent-toolkit:latest

Helm:

helm lint helm/mcp-toolkit
helm upgrade --install mcp-toolkit helm/mcp-toolkit --namespace ai-agents --create-namespace

Default image: ghcr.io/saimudunuri04/mcp-agent-toolkit:latest.

Argo CD (GitOps): argocd/application.yaml syncs helm/mcp-toolkit with automated sync, prune, and self-heal.

End-to-end loop: git push to main → CI runs pytest + golden eval → cd.yml builds and pushes :sha + :latest to GHCR → Argo CD rolls out the new image.

Project structure

mcp_toolkit/          # FastMCP server, MCP client (scoped), LangGraph agent, LLM backends, API
data/docs/            # synthetic docs (5)
data/records.json     # synthetic records (5)
eval/                 # golden-set eval (6 cases) + runner
scripts/              # latency measurement
tests/                # pytest suite (tools, agent, API)
helm/mcp-toolkit/     # Helm chart
argocd/               # Argo CD Application manifest
.github/workflows/    # CI (tests+eval) and CD (build+push to GHCR)

CI status

ci.yml runs pytest + golden eval on every push/PR (mock mode). cd.yml builds and pushes the image to GHCR on merge to main after tests pass.