MCP consulting · MarTech & AdTech

Production MCP servers, shipped.

We design, build, and deploy MCP servers on your infrastructure — so Claude, Gemini, and your in-house agents can reach BigQuery, your DSP, and your CDP.

  • Per-user OAuth & row-level auth
  • Eval harness & structured logs from day one
  • Median time-to-ship: under three weeks

No pitch deck. No follow-up drip. We tell you if MCP isn't the answer.

Trusted credentials — Google AI Leadership ByteByteGo U.S. Army veteran 10+ yrs MarTech / AdTech
$2B+Ad spend touched
12+MCP connectors shipped
10+Years MarTech & AdTech
< 3wMedian time-to-ship
What we do

Four ways to wire up your stack pick one.

All engagements are fixed-fee and time-boxed. We scope, you approve, we ship.

01 / 04

Audit

Two-week diagnostic. We map your data, your agent surface, and where MCP earns its keep.

From $8k →
02 / 04

Build

Production MCP servers deployed on your infrastructure. Auth, logging, evals, and CI from day one.

From $35k →
03 / 04

Retain

Fractional senior MCP engineer for new connectors, agent evolution, and a Slack channel.

$12k / mo →
04 / 04

Enable

Hands-on workshops for your engineering team. We leave with the bus factor at zero.

Custom →
Our approach

Everything we ship is boring on purpose.

Auth, observability, evals, and CI are engineered in from day one. The interesting part is what you wire it to.

Built to be owned

MCP servers your team actually reads, extends, and owns.

No black boxes. Typed inputs, scoped auth, and a repo your engineers can grep.

  • Typed tool schemas
  • Scoped per-user auth
  • CI + container builds
  • Versioned releases
campaigns / server.py
~ uvx --from [email protected] serve
→ registered tools: top_creatives, spend_breakdown
→ auth: oidc:google (scopes: bq:read, ads:read)
→ ready · listening on stdio

claude top creatives last 7d for acme
→ tool: top_creatives(account="acme") 218ms
→ 10 rows · sorted by roas desc
Auth, on the first day

OAuth-per-user, not service accounts that see everything.

Every tool call carries the human identity through the MCP gateway. Audit logs are real. Permissions are real.

  • OIDC / OAuth gateway
  • Scopes per tool
  • Structured audit logs
  • Token rotation
audit.log · liveSOC 2
WhoToolScopes
marek@acmetop_creativesbq:readads:read✓ 218ms
jenna@acmeaudience_overlapbq:read✓ 92ms
j.doe@acmespend_breakdownbq:readdenied
ops@acmepacing_checkads:read✓ 410ms
Where you are

Meeting you where you are.

Four scenarios we see most often. Whichever shape your team is in, we have usually shipped this one before.

01 · Starting from scratch

Brand new to agents?

Where friction shows up

  • Demos that wow in the room, ship to nobody
  • No clear data strategy for agent access
  • Pressure from leadership to do something with AI

Our outcome

We design the architecture, ship the first two connectors, and leave your team with a roadmap they can run.

02 · Agents already up

Context missing?

Where friction shows up

  • Teams paste dashboard numbers into chat
  • Hard-coded tool calls break weekly
  • Each team builds its own integration

Our outcome

We wire your warehouse, CDP, and ad platforms into the agents your team already uses.

03 · Outgrowing no-code

Zapier / Make hitting the ceiling?

Where friction shows up

  • Brittle workflows breaking at scale
  • Rate limits and quotas
  • No observability beyond did it run?

Our outcome

We replace glue with typed, owned MCP connectors, often paying for itself in vendor savings.

04 · Scaling across teams

MCP everywhere, governance nowhere?

Where friction shows up

  • Every team runs its own server
  • No shared auth, no shared logs
  • Compliance asks questions you cannot answer

Our outcome

We build the gateway pattern: one MCP front door, central observability, and team handoff.

Why MCP, why now

Your data is the moat.
Agents are the new way through it.
MCP is the bridge.

Most teams are still drawing that bridge on whiteboards. We build the production ones with the boring, important things engineered in: per-user auth, eval harnesses, structured logs, and CI.

Architecture

One gateway. Many bridges.

We host a single MCP gateway per environment. Every connector is just a server behind it.

Hover any node →
trace the flow
YOUR DATA YOUR AGENTS MCP MCP gateway AUTH · LOGS · EVAL BQBigQuery DBMongoDB TBTableau ADMeta / Google Ads CCClaude Code CDClaude Desktop GMGemini CUCursor GPChatGPT
The shape of it

What a production MCP server actually looks like.

Boring on purpose. Typed inputs, scoped auth per user, structured logs, and an eval harness in the repo.

from mcp.server import Server
from .auth import scoped_bq_client

# A read-only window into the campaigns warehouse.
server = Server("campaigns", version="1.4.0")

@server.tool()
async def top_creatives(account_id: str, window: str = "7d"):
    """Return the top 10 creatives by ROAS over a window."""
    async with scoped_bq_client() as bq:
        rows = await bq.run(QUERY_TOP_CREATIVES, account_id, window)
    return render(rows)
import { Server } from "@modelcontextprotocol/sdk";
import { scopedBQ } from "./auth";

const server = new Server({ name: "campaigns", version: "1.4.0" });

server.tool("top_creatives", {
  description: "Top 10 creatives by ROAS",
  input: z.object({ accountId: z.string(), window: z.string() }),
  handler: async ({ accountId, window = "7d" }) => {
    const bq = await scopedBQ();
    const rows = await bq.run(Q_TOP, accountId, window);
    return { content: [{ type: "text", text: render(rows) }] };
  }
});
mcpServers:
  campaigns:
    command: "uvx"
    args: ["--from", "[email protected]", "serve"]
    env:
      MCP_GATEWAY_URL: "https://mcp.acme.corp"
      MCP_AUTH: "oidc:google"
    scopes: ["bq:read", "ads:read"]
    audit: true
    evals: "campaigns/evals.yaml"
We connect

Your stack, on speaking terms with every agent.

Don't see yours? Most MCP connectors are a day or two of focused work.

Agents

Claude CodeClaude DesktopGeminiCursorChatGPT / OpenAI

Data

BigQueryMongoDBTableauSnowflake

Ad platforms

Meta AdsGoogle AdsBing AdsTaboolaVoluum
Curious?

Try a real MCP call against our public gateway.

We host a tiny demo MCP at playground.mcpbuilders.dev with three read-only tools over open MarTech datasets.

~/playground · live
$ claude mcp add playground https://playground.mcpbuilders.dev
→ connected · 3 tools registered

claude show me top campaigns from the demo dataset
→ tool: campaigns.top_creatives 218ms
→ tool: campaigns.spend_breakdown 92ms

Top 3 creatives by ROAS over the last 7 days:
  1. video_demo_v2 — ROAS 4.8, $12k spend
  2. carousel_holiday — ROAS 3.9, $8k spend
  3. static_v7 — ROAS 3.2, $14k spend
Who you'll work with

Marek Bejda. Builds the bridges.

Army veteran. Ten years shipping infra and data systems for MarTech and AdTech. Spent the last year putting production MCP servers in front of agents that actually have to earn their compute.

When teams hire mcpbuilders, you are not getting handed off to a junior. You work directly with me, end to end.

CertificationsGoogle AI Leadership · ByteByteGo Bootcamp
BackgroundU.S. Army veteran · 10+ years MarTech & AdTech engineering
Ready?

Audit Monday. Production MCP in three weeks.

Thirty-minute call. We map your stack, name the first connector to ship, and give you a fixed quote by Friday. If MCP isn't the answer, we tell you.