What is this course about?
MCP standardizes how agents reach tools and data, which removes a large class of bespoke integration work. This course covers server design, the authentication and per-user authorization that enterprise deployment requires, wrapping legacy systems with no modern API, and the rate limiting and auditing that belong at the protocol layer.
Who is this course for?
- Integration and platform engineers
- Enterprise architects
- API and middleware teams
- AI engineering leads
What do I need before starting?
- Strong API and integration background
- Familiarity with OAuth and enterprise identity
What will I be able to do afterwards?
- Explain what MCP standardizes and which integration problem it removes
- Design MCP servers choosing correctly between resources, tools, and prompts
- Implement per-user authorization rather than a shared service account
- Wrap legacy systems that expose no modern API
- Apply rate limiting, auditing, and cost control at the protocol layer
What does each module cover?
What problem does MCP remove?
40 minThe bespoke-integration cost that a standard protocol eliminates, and what it does not solve.
Objectives
- Describe the integration problem before standardization
- State what MCP does and does not address
- Decide where MCP is the right choice
Topics
Activity. Inventory your existing agent integrations and identify which MCP would replace.
How do server, client, and transport fit together?
50 minThe architecture in practice, including deployment topology inside a private network.
Objectives
- Describe the component architecture
- Choose transport appropriate to the deployment
- Plan topology for a private network
Topics
Activity. Deploy a minimal MCP server and connect a client over your chosen transport.
Resources, tools, or prompts — which primitive?
50 minChoosing the right primitive, since the wrong choice makes an integration awkward forever.
Objectives
- Distinguish the three primitives and their semantics
- Choose correctly for a given capability
- Refactor a badly-chosen primitive
Topics
Activity. Model one internal system across all three primitives and evaluate which fits.
How do you authorize per user, not per service account?
60 minThe requirement that separates a demo MCP server from an enterprise one.
Objectives
- Propagate user identity through the protocol
- Enforce per-user authorization at the server
- Avoid the shared service account anti-pattern
Topics
Activity. Implement per-user authorization and verify two users get different results.
How do you wrap a legacy system?
55 minExposing systems with no modern API — mainframes, fixed-format files, and screen-scraped interfaces.
Objectives
- Wrap systems lacking a modern API
- Handle brittle interfaces safely
- Set realistic reliability expectations
Topics
Activity. Wrap a legacy interface as an MCP server with honest error semantics.
What belongs at the protocol layer?
50 minRate limiting, auditing, and cost control implemented once rather than per agent.
Objectives
- Implement rate limiting at the server
- Emit audit records for every tool call
- Attribute cost per user and per capability
Topics
Activity. Add rate limiting and audit emission, then verify both under load.
How do you secure an MCP server?
50 minThe security work specific to exposing internal capability to an agent.
Objectives
- Apply least privilege to exposed capabilities
- Prevent injection through tool parameters
- Design for containment if an agent is compromised
Topics
Activity. Attack your own MCP server through tool parameters and remediate what works.
Building a production MCP server
65 minThe build module: a server exposing an internal system with row-level permissions.
Objectives
- Build a server with row-level permission enforcement
- Include audit, rate limiting, and cost attribution
- Document it for other teams to consume
Topics
Activity. Build the server and prove row-level enforcement across three user roles.
What is the capstone project?
Production MCP server with per-user authorization
Build an MCP server exposing a real internal system, with identity propagation, row-level authorization, audit emission, rate limiting, cost attribution, and documentation for consuming teams.
Deliverable: A deployed server with row-level authorization proven across roles, plus consumer documentation.
How are learners assessed?
- Row-level authorization verified — different users must get different results
- Parameter injection attempts documented with remediations
- Audit records reviewed for sufficiency to reconstruct a session
What ships with the course?
Facilitator guide
Session-by-session running order, discussion prompts, and the questions that reliably derail a room.
Learner workbook
Exercises, checklists, and the templates each module's activity produces.
Hands-on lab environment
A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots.
Assessment bank
Scenario questions and rubric criteria mapped to each stated learning outcome.
Source bibliography
Every primary regulation and standard cited on this page, linked and dated.
Which AI agents does this course use?
The hands-on modules run against agents already deployable on the ibl.ai platform for enterprise.
Where does the course material come from?
Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time.
- Model Context Protocol
Anthropic
The protocol specification the entire course is built on.
- Introducing the Model Context Protocol
Anthropic
Design rationale and intended usage patterns.
- OWASP Top 10 for LLM Applications
OWASP
Security requirements applied to server design in Module 7.
- NIST SP 800-53 Rev. 5
NIST
Access control and audit control families the server implements.
Delivery notes
Binding guidance for anyone preparing and delivering this course.
- Module 4 is the enterprise differentiator. Most MCP material assumes a single-user desktop context; per-user authorization in a multi-tenant enterprise deployment is where real projects stall.
- The protocol evolves. Pin the specification version taught, note it visibly, and re-verify every build module against the current spec at each revision.
- Module 5 needs a genuinely awkward legacy system. A REST API wrapped in MCP teaches nothing; a fixed-format batch interface teaches the actual skill.
- Do not present MCP as solving agent reliability. It solves integration; reliability is ENT-1's subject, and conflating them oversells the protocol.
- Ship the server scaffold as a reusable artifact with authorization and audit already wired in — that is the part teams get wrong when starting from a blank file.
Why run AI training on a platform you own?
You own the course, not a licence to it
Course content, learner data, and the platform run inside your perimeter — you own all the code and the data.
Model-agnostic delivery
Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime.
No per-seat training licences
Usage-based or self-hosted, so cost tracks actual use rather than headcount.
Deploy anywhere
Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools.
Frequently asked questions
What does the Model Context Protocol: Connecting Agents to Enterprise Systems course cover?
MCP standardizes how agents reach tools and data, which removes a large class of bespoke integration work. This course covers server design, the authentication and per-user authorization that enterprise deployment requires, wrapping legacy systems with no modern API, and the rate limiting and auditing that belong at the protocol layer. It runs 6.5 hours across 8 modules across 8 modules, at advanced level, and closes with a capstone: Production MCP server with per-user authorization.
Who should take Model Context Protocol: Connecting Agents to Enterprise Systems?
It is written for Integration and platform engineers, Enterprise architects, API and middleware teams, AI engineering leads. Prerequisites: Strong API and integration background; Familiarity with OAuth and enterprise identity.
Can we run this course on our own infrastructure?
Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for enterprise teams that cannot send work to a public AI tool.
How do we get access to Model Context Protocol: Connecting Agents to Enterprise Systems?
Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter.
How much does AI training for enterprise cost on ibl.ai?
There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.