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Ayush Kumar
Ayush Kumar

Posted on Originally published at logiclooptech.dev

Model Context Protocol Examples: How to Run MCP with FastAPI

#ai #agents #mcp #fastapi

Quick answer

If you need a lightweight way to ship rich context (documents, embeddings, user metadata) to LLM providers without reinventing the wheel, the Model Context Protocol (MCP) does exactly that. You spin up a small HTTP server, push chunks of context, retrieve a signed token, and hand that token to OpenAI or Anthropic. The rest of this post shows model context protocol examples from Docker to FastAPI, plus the pitfalls I’ve hit in production.

What is the Model Context Protocol (MCP) and why does it matter?

MCP is an open-source spec that defines a JSON-over-HTTP contract for storing, retrieving, and version-controlling prompt context. Instead of stuffing a 32 KB prompt into every LLM request, you store the heavy parts (large documents, vector indexes, user-specific settings) in a dedicated service and reference them by a short token.

Why it matters:

  • Cost control – you pay LLM providers only for the token, not the megabytes of raw text.
  • Consistency – the same context can be reused across multiple calls, ensuring deterministic answers.
  • Security – the service can enforce access policies, redact sensitive fields, and rotate keys without touching the LLM code.

In short, MCP lets you treat context as a first-class resource instead of a hacky string concatenation.

How do I set up a local MCP server with Docker?

Running MCP locally is a single-command affair. The official image ships with a tiny SQLite store for dev, and a configurable PostgreSQL backend for prod.

# 1. Pull the image
docker pull ghcr.io/mcp-dev/mcp-server:latest

# 2. Run with an in‑memory store (good for quick tests)
docker run -d \
  -p 8080:8080 \
  -e MCP_STORE=sqlite \
  -e MCP_AUTH_TOKEN=dev-secret \
  ghcr.io/mcp-dev/mcp-server:latest
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For a more realistic setup, mount a volume and point it at PostgreSQL:

docker run -d \
  -p 8080:8080 \
  -e MCP_STORE=postgres \
  -e POSTGRES_DSN="postgresql://mcp:pwd@db:5432/mcp" \
  -e MCP_AUTH_TOKEN=prod-secret \
  --restart unless-stopped \
  ghcr.io/mcp-dev/mcp-server:latest
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The container logs show a health check on http://localhost:8080/health. If you see {"status":"ok"} you’re ready to start feeding it context.

What I’ve been bitten by: The default SQLite file lives inside the container. When the container restarts you lose everything. Bind-mount a host directory (-v $(pwd)/data:/data) or switch to Postgres before you go beyond a toy demo.

How can I use MCP to pass context to OpenAI/Anthropic LLMs in Python?

The Python client is tiny (≈ 30 KB) and works with both sync and async code. Below is a minimal example that uploads a document, fetches a token, and calls the OpenAI ChatCompletion endpoint.

import httpx
import json
from openai import OpenAI

MCP_URL = "http://localhost:8080"
MCP_TOKEN = "dev-secret"          # same as -e MCP_AUTH_TOKEN
OPENAI_API_KEY = "sk-..."

client = httpx.Client(headers={"Authorization": f"Bearer {MCP_TOKEN}"})

def upload_context(name: str, content: str) -> str:
    resp = client.post(
        f"{MCP_URL}/v1/context",
        json={"name": name, "content": content},
    )
    resp.raise_for_status()
    return resp.json()["context_id"]

def get_context_token(context_id: str) -> str:
    resp = client.post(
        f"{MCP_URL}/v1/token",
        json={"context_id": context_id, "expires_in": 300},
    )
    resp.raise_for_status()
    return resp.json()["access_token"]

# 1️⃣ Upload a long FAQ
ctx_id = upload_context(
    name="support_faq",
    content=open("support_faq.txt").read()
)

# 2️⃣ Get a short-lived token
token = get_context_token(ctx_id)

# 3️⃣ Call OpenAI, passing the token in `metadata`
client_oai = OpenAI(api_key=OPENAI_API_KEY)
completion = client_oai.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You have access to supplemental context via the `context_token` field."},
        {"role": "user", "content": "Explain our refund policy."}
    ],
    metadata={"context_token": token}
)

print(completion.choices[0].message.content)
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The metadata.context_token field is a convention adopted by the LLM provider to fetch additional data on demand. Anthropic follows the same pattern, just swap the client library.

Trade-off: You add an extra network hop. In latency-critical paths (sub-100 ms) you might keep the most recent snippet in-process instead of going through MCP.

How do I integrate MCP calls into a FastAPI endpoint?

Embedding MCP into FastAPI is straightforward because both use standard ASGI patterns. Below is a production-ready endpoint that:

  1. Accepts a user query.
  2. Looks up or creates a context record based on the user’s organization.
  3. Calls OpenAI with the generated token.
  4. Returns the LLM answer. 
from fastapi import FastAPI, HTTPException, Depends
from pydantic import BaseModel
import httpx
from openai import AsyncOpenAI

app = FastAPI()
mcp_client = httpx.AsyncClient(base_url="http://mcp:8080",
                               headers={"Authorization": "Bearer prod-secret"})
openai_client = AsyncOpenAI(api_key="sk-...")

class QueryPayload(BaseModel):
    user_id: str
    organization_id: str
    question: str

async def get_or_create_context(org_id: str) -> str:
    # Try to fetch an existing context_id from DB (pseudo‑code)
    ctx_id = await fetch_context_id_from_db(org_id)
    if ctx_id:
        return ctx_id

    # Fallback: load org‑specific docs from S3, upload to MCP
    docs = await load_org_docs_from_s3(org_id)
    resp = await mcp_client.post(
        "/v1/context",
        json={"name": f"org-{org_id}", "content": docs}
    )
    resp.raise_for_status()
    ctx_id = resp.json()["context_id"]
    await store_context_id_in_db(org_id, ctx_id)
    return ctx_id

@app.post("/answer")
async def answer(payload: QueryPayload):
    ctx_id = await get_or_create_context(payload.organization_id)
    token_resp = await mcp_client.post(
        "/v1/token",
        json={"context_id": ctx_id, "expires_in": 120}
    )
    token_resp.raise_for_status()
    token = token_resp.json()["access_token"]

    try:
        completion = await openai_client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {"role": "system", "content": "Use supplemental context via `context_token`."},
                {"role": "user", "content": payload.question}
            ],
            metadata={"context_token": token}
        )
    except Exception as exc:
        raise HTTPException(status_code=502, detail=str(exc))

    return {"answer": completion.choices[0].message.content}
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Notice the separation of concerns:

  • MCP client lives as a singleton, reusing HTTP connections.
  • Context creation is lazy – only the first request for a given org hits S3.
  • Error handling bubbles up as 502 so the caller knows the LLM side failed.

If you’re already familiar with async SQLAlchemy, you can slot the fetch_context_id_from_db and store_context_id_in_db calls into the same session you use for other business data. I ran into a MissingGreenlet error when mixing sync and async DB calls; the fix was to keep everything async or run the sync path in a threadpool. See my post on that here.

What are the best practices, security concerns, and troubleshooting steps for MCP in production?

Authentication & Authorization

  • Use a short-lived bearer token for each service (rotate every 24 h). Store the secret in a secret manager, not in Dockerfiles.
  • Scope tokens to a single organization or use-case. The MCP spec supports audience claims; enforce them in your gateway.

Data Hygiene

  • Strip PII before uploading. MCP does not scrub data for you.
  • Version context IDs. When a document changes, create a new ID and keep the old one for a grace period. This avoids breaking in-flight LLM calls.

Observability

  • Enable the built-in /metrics endpoint (Prometheus format) and scrape mcp_contexts_total, mcp_token_requests, and mcp_request_latency_seconds.
  • Correlate MCP logs with your FastAPI request IDs. I added a X-Request-ID header to every inbound call and propagated it downstream; without it I couldn’t tell which token request caused a timeout.

Failure Modes

Symptom Likely cause Fix
502 from /answer after a deploy MCP container restarted, losing SQLite store Switch to Postgres, mount a volume, or add a health-check retry loop
Token validation error from OpenAI Token expired before LLM could fetch context Increase expires_in or pre-warm the token earlier in the request flow
Spike in latency > 500 ms Network congestion between FastAPI and MCP Co-locate containers in the same pod or VPC, enable HTTP/2 keep-alive

Cost considerations

  • Each token fetch is a cheap HTTP call, but every context upload consumes storage. Set a TTL (e.g., 30 days) on unused contexts via the /v1/context/expire endpoint.
  • If you’re hitting rate limits on the LLM side, remember that MCP reduces prompt size, which can lower token-based pricing.

When NOT to use MCP

  • Real-time audio transcription where the context is a few seconds of audio – the overhead of an extra service outweighs the benefits.
  • One-off scripts that run infrequently; a simple file read is cheaper and easier.

How does a context-aware AI assistant look when built with MCP?

Below is a stripped-down version of the assistant I deployed for a SaaS support team. The assistant:

  1. Pulls the latest knowledge-base markdown from a private Git repo.
  2. Stores it in MCP under a daily versioned ID.
  3. Serves answers via a FastAPI endpoint that the internal chatbot UI calls. 
# cron_job.py – runs nightly
import httpx, os, subprocess, datetime

MCP_URL = "http://mcp:8080"
TOKEN = os.getenv("MCP_TOKEN")
client = httpx.Client(base_url=MCP_URL,
                     headers={"Authorization": f"Bearer {TOKEN}"})

def refresh_kb():
    # Pull latest docs
    subprocess.run(["git", "pull"], cwd="/opt/kb", check=True)
    with open("/opt/kb/combined.md") as f:
        content = f.read()

    version = datetime.date.today().isoformat()
    resp = client.post(
        "/v1/context",
        json={"name": f"support_kb_{version}", "content": content}
    )
    resp.raise_for_status()
    ctx_id = resp.json()["context_id"]
    # Store the latest ID somewhere reachable by FastAPI
    with open("/tmp/latest_kb_id.txt", "w") as f:
        f.write(ctx_id)

if __name__ == "__main__":
    refresh_kb()
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FastAPI uses the latest ID on each request:

@app.get("/support")
async def support(question: str):
    with open("/tmp/latest_kb_id.txt") as f:
        ctx_id = f.read().strip()
    token = await get_context_token(ctx_id)   # same helper as earlier
    resp = await openai_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role":"user","content":question}],
        metadata={"context_token": token}
    )
    return {"answer": resp.choices[0].message.content}
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The result? The assistant can answer “How do I reset my password?” using the full knowledge base without ever sending the megabytes of markdown to OpenAI. The latency stayed under 300 ms even during peak traffic. I later wrote a post on turning this pattern into a stock-analysis agent; you can read it here.

FAQ

What format does MCP expect for context data?

A plain JSON payload with name (string) and content (string). You can also send metadata for custom indexing; the spec supports a type field for binary blobs.

Can I use MCP with other LLM providers besides OpenAI and Anthropic?

Yes. As long as the provider accepts a token in the request metadata and knows how to resolve it via the MCP endpoint, it works. Some vendors require the token in a custom header – just adjust the client code.

Is MCP thread-safe when I run many FastAPI workers?

The server itself is stateless aside from the backing store, so concurrent requests are safe. The client side must reuse an httpx.AsyncClient or requests.Session to avoid socket exhaustion.

How do I purge old contexts automatically?

Call the /v1/context/expire endpoint with a cutoff date, or configure a background job that deletes rows older than your retention policy. The spec also supports a TTL on upload (expires_in), which triggers automatic cleanup.

Key Takeaways

  • MCP lets you externalize large prompt context, cutting LLM token costs and improving consistency.
  • A single Docker command gets a dev server running; switch to Postgres for any serious workload.
  • The Python client handles uploads, token retrieval, and works with both OpenAI and Anthropic.
  • FastAPI integration is a matter of reusing an async HTTP client and wiring a tiny helper to fetch tokens.
  • Secure the service with short-lived bearer tokens, version your contexts, and monitor latency.
  • Use MCP for any scenario where context size exceeds the LLM’s prompt limit or where you need reusable, auditable data.

With these model context protocol examples you should be able to prototype tomorrow and ship a stable production service next sprint. Happy coding.

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