Governed MCP — agent infrastructure
Production Model Context Protocol servers so Claude, Cursor, and internal agents can reach CRM, EHR, ERP, and proprietary APIs under authentication, least privilege, and an audit trail — not a chatbot retainer and not a paste into ChatGPT.
Model Context Protocol (MCP) is an open protocol that standardizes how AI applications connect to tools, data sources, and prompts. An MCP server exposes those capabilities so clients can call tools, read resources, and use prompts over a consistent interface — instead of a one-off plugin for every system. Maxiom builds production MCP servers for organizations that cannot use off-the-shelf connectors: proprietary APIs, HIPAA/PHI boundaries, federal data classification, and legacy systems that must stay in place. This is the build-side product in Maxiom's governed AI stack — after an AI Code Audit when the question is still “what did Copilot write,” and before a generic chatbot retainer. Healthcare EHR and PHI programs pair with Maxiom Labs at https://www.maxiomlabs.com/.
The problem
People paste CRM notes, tickets, and documents into ChatGPT because the approved agent cannot see internal systems. That is a data-loss event waiting on a screenshot.
Public connectors cover GitHub, Slack, and generic APIs. They do not cover your proprietary services, EHR, or the authorization model your security team will actually sign off on.
Leadership wants Claude or Cursor on internal data. InfoSec will not allow unbounded tool access. You need least privilege, audit trails, and a threat model — not another demo.
Agents cannot help until they can query the system of record. Rewriting that system first is years of delay. A governed MCP wrap can expose what the agent needs without a big-bang migration.
What we build
Senior engineers design the context layer your agents will actually be allowed to use — tools, resources, and prompts with authentication, authorization, and evidence for the next security review.
Tools, resources, and prompts tailored to your APIs and workflows — not a generic filesystem server pointed at production.
Identity, least-privilege tool scopes, and immutable logs for every agent invocation so security questionnaires have code-level answers.
EHR, clinical, and healthcare APIs with minimum-necessary access. Healthcare programs pair with Maxiom Labs (maxiomlabs.com).
MCP in front of mainframe, .NET Framework, on-prem, or undocumented interfaces so agents can query without a rewrite.
Governed access to Salesforce, HubSpot, SAP, NetSuite, and internal equivalents — with field-level constraints, not dump-the-database tools.
stdio, HTTP, and remote transports fitted to Claude, Cursor, and custom agents, plus failure modes when a tool must not run.
You have executive air cover for agents. InfoSec will not approve ChatGPT-on-production-data. You need a context layer they can review: scoped tools, identity, and an audit trail.
Clinicians or ops want an agent on EHR or care-management data. PHI cannot leave the boundary. You need MCP with minimum-necessary access and BAA-aware engineering — see Maxiom Labs at maxiomlabs.com.
The answers live in a system nobody wants to touch. You need agents to query it safely now, not after a three-year rewrite. An MCP wrap is the governed interface.
Kickoff
We pin down which agents, which systems of record, which data classes, and which security review is blocking rollout.
You get: a written agent-and-system map
What the agent must never do is decided before a server is built — scopes, identity, and an audit trail.
You get: a threat model InfoSec can review
MCP servers, transports, and tool/resource/prompt design — tested against real (or faithful) target systems.
You get: a working context layer
Audit evidence, runbooks, failure modes, and ownership so your team can operate the servers after the engagement.
You get: servers your team can operate
Which clients (Claude, Cursor, custom), which systems of record, which data classes, and what the agent must never do. Written scopes and least-privilege tool lists before any server is built.
Tools, resources, and prompts with authentication, authorization, audit logging, and tests against real target systems — in your environment, not a weekend protocol experiment.
Runbooks, failure modes, and ownership so your team can run the context layer. Optional retainer to add tools as new systems come online. Healthcare EHR and PHI work continues with Maxiom Labs.
A defined server (or set of servers), auth model, audit trail, tests, and handoff. Typical when security is blocking an agent rollout and you need a governed context layer — not a chatbot wrapper.
The initial server, then a monthly bucket to add tools, tighten scopes, and support new agent clients. PHI and clinical systems are scoped with Maxiom Labs (https://www.maxiomlabs.com/) rather than as a generic AI subscription.
If the question is what Copilot, Cursor, or Claude Code already wrote into the repo, start with an AI Code Audit, then AI Code Oversight if you want a monthly review cadence. Buy MCP Development when agents need governed runtime access to CRM, EHR, ERP, or internal APIs. Most new logos do not start here unless security is already blocking an agent rollout.
No. MCP development is production agent infrastructure: servers, auth, least privilege, and audit trails. We do not sell chatbot retainers or wrap ChatGPT around your data. If you need a HealthTech agent on EHR or PHI, that program runs with Maxiom Labs (https://www.maxiomlabs.com/) alongside this engagement.
MCP is an open protocol — originally published by Anthropic — for exposing tools, resources, and prompts to AI applications. MCP development is the design and implementation of those servers so Claude, Cursor, and custom agents can reach internal systems under authentication, authorization, and audit. Maxiom builds custom servers when official or community connectors cannot cover proprietary APIs, PHI, or legacy interfaces.
Use off-the-shelf servers when the system is public, the data is not sensitive, and the vendor already ships a maintained connector. Build custom when you have proprietary APIs, BAAs and PHI, federal classification, undocumented legacy interfaces, or security requirements that block generic tool access.
AI development covers models, products, and workflows. MCP development is the context layer those agents use: which tools they can invoke, which data they can read, and how every call is authenticated and logged. Many programs need both.
An AI code audit reviews code that Copilot, Cursor, or similar tools wrote into your repository. MCP development builds the servers and tool interfaces agents use at runtime. If agents will also generate product code, pair MCP work with an AI Code Audit or ongoing oversight.
Yes, when scoped that way — minimum-necessary tool access, encryption, audit logging, and BAA-aware boundaries. Healthcare MCP work pairs with HIPAA Software Development and Maxiom Labs (https://www.maxiomlabs.com/) for the dedicated HealthTech practice.
Often yes. A well-designed MCP server can sit in front of mainframe, .NET Framework, on-prem, or undocumented APIs so agents query through a governed interface. Broader platform migration still lives under Legacy Modernization when the runtime itself must change.
No. Client systems stay in client environments. We do not process proprietary source or PHI through third-party AI as part of delivery. Tool access is least-privilege and scoped in writing after NDA.
Scoped servers (tools, resources, prompts), authentication and authorization, audit logging, tests, operational runbooks, and a handoff so your team can operate and extend the context layer. Threat modeling happens before build, not after the first agent demo. Engagements start with a scoping call covering clients, systems, data classification, and the security review that is blocking rollout.
Governed MCP
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