I build Model Context Protocol (MCP) servers — the integration layer that connects AI agents (Claude, ChatGPT, and custom LLM apps) to the APIs, databases, and internal tools they need to actually act on real systems instead of simulating that they do.
Proof you can check before you reply
Lead with repos and test counts, not adjectives. Every number below is real and verifiable.
What keeps a server out of the 2am pager rotation
Most teams know they want their agent wired into their stack. The hard part is building the production layer correctly — and that is the whole job.
Scoped to your specific tools and APIs, with typed tool definitions an agent can call reliably.
At the right trust boundary — API-key, OAuth, or session-scoped — so an agent can't read across the wall.
Backend-down, rate-limit, timeout — each returns a clean structured error. The agent session lives.
So an upstream API change doesn't silently break the integration weeks after handoff.
Runs green on a clean checkout — not a demo that breaks on the second request.
Your team can extend the server without the original author. No bus-factor of one.
Fixed-scope, not open-ended hourly
Every engagement is scoped and quoted flat. Start with a low-risk audit; the fee credits toward the build if you proceed. A typical Sprint ships a working, tested server in about two weeks.
How an engagement runs
A short paid spike de-risks the build before either of us commits to a fixed quote.
A read of your stack delivered as a written MCP spec + risk log. You ship the right server the first time; the fee credits toward the build.
~1–2 weeks. Auth-scoped, fail-soft, version-pinned, tested. You see it run green on a clean checkout — not just on my machine.
Handoff docs so your team extends the server without me. Optional retainer keeps it green as upstream APIs and the MCP spec change.
How I work, shown in real builds
No fabricated client wins. These are my own public, tested builds — and an honest look at how I validate (and kill) systems.
A manifest-driven MCP scaffolder + runtime. 179 passing tests, public repo, 90-second Loom demo of a live test run.
Read the case study →The exact common client ask: a read/write MCP over the GitHub REST API. Write tools off by default, typed rate-limit handling, 74 passing tests, public repo.
Read the case study →OS-level control when there's no API: screenshot, windows, input, screen-recording. Input off by default, rate-capped, 121 passing tests, public repo.
Read the case study →Retrieval-augmented search exposed as an MCP tool. Local ONNX embeddings, fail-soft, 50 tests, $0 inference cost.
Read the case study →The reliability story: how I forward-test systems honestly and retire the ones that don't earn their keep. Rigor, not returns.
Read the case study →Before you reach out
No. Work is fixed-scope sprints, scoped and quoted flat after a short call. The entry point is a $1,500 MCP Readiness Audit (written report, no code), credited toward a Sprint if you proceed within 60 days.
A typical Integration Sprint ships a working, tested server in about two weeks. Multi-source and self-hosted builds are larger and scoped separately.
The audit covers that too — a read of the existing server for auth-scoping gaps, fail-soft handling, version-pinning, and test coverage, delivered as a written risk log. The MCP spec is still moving; existing servers will need updates as it changes.
Python and Node MCP servers over REST, on cloud or self-hosted / on-prem. Auth via API-key, OAuth, or session-scoping. If your tools speak HTTP, they can be wired.
That's the whole point. mcp-factory is public with 179 passing tests, and there's a 90-second Loom demo showing the test run, auth-scoping, and fail-soft handling live.
Not yet — and I won't fabricate any. Instead I publish tested code you can check yourself and a case study on how I validate systems honestly (including killing ones that don't work). That's the trust signal, in place of borrowed logos.
Send me the two or three tools you need an agent to actually use. I'll tell you the right scope and a flat quote — after I understand the build, not before.
Book a scoping call