# AI Data Engineering - Forward-Deployed Engineers (FDEs)

> Source: https://ibl.ai/service/data-engineering/k12

Forward-Deployed Engineers build your MCP-powered district memory layer for AI agents — with your data, in your environment.

Build your district "memory layer" for AI agents — powered by the Model Context Protocol (MCP) — with your data, in your environment.

## What This Is

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ibl.ai's Forward-Deployed Engineers embed with your team to connect SIS, LMS, rostering, assessment, identity, storage, and curriculum systems into a secure, policy-aware memory layer built on the Model Context Protocol (MCP). That memory becomes the backbone for AI agents — student tutors, teacher assistants, and district admin tools — running privately in your infrastructure.

This is professional services, billed by the hour (ultra-competitive rates), with clear milestones and artifacts you own.

## Built on the Model Context Protocol (MCP)

### What is MCP?

The Model Context Protocol is an open standard — originally developed by Anthropic — that defines how AI models connect to external data sources, tools, and services. MCP provides a universal interface between AI agents and your district systems, replacing brittle custom integrations with standardized, secure connectors.

Think of MCP as USB-C for AI: one protocol, every system. Instead of building a custom integration for each SIS, LMS, or rostering tool, MCP gives agents a single, consistent way to read data, call tools, and respect permissions across your entire district stack.

### Why MCP Matters for K-12 Districts

District IT teams maintain dozens of systems — PowerSchool, Google Classroom, Clever, NWEA MAP, and more. Traditional integration approaches require point-to-point connectors that break with every vendor update. MCP eliminates this fragility by providing a protocol-level contract between AI agents and data sources.

With MCP, your district gets portable agents that work across any LLM provider (OpenAI, Anthropic, Google, Meta, or local models), interchangeable connectors that can be swapped without rewriting agent logic, and built-in security boundaries where every data access goes through policy-aware middleware with COPPA/CIPA/FERPA-grade controls.

### MCP Architecture at ibl.ai

Every ibl.ai deployment uses MCP as the core integration protocol. Our Forward-Deployed Engineers build MCP servers for each district system — SIS, LMS, rostering, assessments, identity providers, and document stores. These MCP servers expose structured tools and resources that agents can discover and invoke at runtime.

The result is a composable agent architecture: a student tutor agent can query enrollment data from PowerSchool, fetch assignments from Google Classroom, check assessment scores from NWEA MAP, and retrieve IEP accommodations — all through MCP — without any custom glue code between systems.

## MCP Servers We Build

### SIS MCP Server (PowerSchool, Infinite Campus, Skyward, Aeries)

Exposes enrollment records, grade histories, attendance data, IEP/504 plans, and demographic information as MCP resources. Agents can query real-time student data without direct database access. Field-level FERPA/COPPA controls enforce who sees what based on role and parental consent.

### LMS MCP Server (Google Classroom, Canvas, Schoology, Brightspace)

Provides assignments, grades, class materials, discussion posts, and rubrics as MCP tools. Agents can retrieve specific lesson materials, check submission deadlines, and access classroom-level analytics — all scoped to the requesting user's permissions and age-appropriate filters.

### Rostering MCP Server (Clever, ClassLink, OneRoster)

Connects class rosters, teacher assignments, school schedules, and organizational hierarchies. Agents can resolve student-teacher relationships, pull section enrollments, and maintain accurate class groupings across semester changes.

### Identity & Directory MCP Server (Google Workspace for Education, Microsoft 365, Clever SSO)

Provides role resolution, group memberships, and authentication context. MCP-level RBAC ensures agents only access data appropriate for the authenticated user's role — student, teacher, counselor, or administrator — with parental consent enforcement for minors.

### Assessment MCP Server (NWEA MAP, Renaissance Star, iReady)

Indexes assessment scores, growth metrics, grade-level proficiency, and diagnostic data — and makes them retrievable via secure MCP queries. Agents can cite specific performance benchmarks to personalize tutoring and flag students for intervention.

### Custom MCP Servers

We build MCP servers for any system with an API or database: behavior tracking (PBIS platforms), transportation, food services, special education case management, state reporting systems, and more. If your district has it, we can connect it.

## MCP Security and Governance

### Protocol-Level Access Control

Every MCP request carries authentication context — who is asking, what role they hold, and what parental consent has been granted. Our MCP middleware enforces field-level permissions before data ever reaches the agent.

A student asking about their own grades sees their records; a teacher querying the same system sees their classroom; a principal sees school-wide analytics. Same MCP server, different views.

### PII Masking and Data Minimization

MCP responses pass through a policy engine that redacts sensitive fields based on configurable rules. Social security numbers, disability classifications, disciplinary records, and family information are masked or excluded from agent context unless explicitly authorized by policy.

Every redaction decision is logged for audit.

### Audit Trails and Compliance

Every MCP tool invocation is logged with timestamp, requesting agent, authenticated user, data accessed, and response summary.

These audit trails support COPPA/CIPA/FERPA compliance reviews, state student privacy law audits, and incident response. Logs are stored in your infrastructure and retained per your district policy.

### Sandboxed Execution

MCP servers run in isolated containers within your VPC or on-premises infrastructure. No student data leaves your environment.

Agents interact with MCP servers over internal networks with mTLS encryption. External LLM providers receive only the agent's synthesized prompts — never raw student data.

## Who We Work With

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District IT & Technology Directors

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Curriculum & Instruction Leaders

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Student Services / Counseling

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Special Education Coordinators

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School Principals / Building Leaders

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Privacy Officers, Legal, Board

## What We Do (Scope at a Glance)

### Systems & Data Mapping

Inventory: SIS (e.g., PowerSchool, Infinite Campus, Skyward), LMS (Google Classroom, Canvas, Schoology), Rostering (Clever, ClassLink, OneRoster), Assessments (NWEA MAP, Renaissance Star, iReady), Identity (Google Workspace/Microsoft 365/Clever SSO), Storage (Google Drive/OneDrive/S3).

Schemas & Contracts: enrollment, rosters, grades, IEP/504 plans, attendance, assessment scores, curriculum metadata. Policy & Governance: COPPA/CIPA/FERPA fields, parental consent, role scopes, retention rules, redaction maps.

### MCP Server Development

We build production-grade MCP servers for every district system in your stack. Each server exposes typed tools and resources following the MCP specification, with built-in schema validation, error handling, rate limiting, and observability.

Servers are containerized and deployed via Terraform or Kubernetes manifests you own.

### Memory Layer Engineering

MCP-based Connectors: secure adapters that normalize read/write paths across systems. Per-Student Memory Graph: knowledge graph + vector index for contextual retrieval (curriculum content, assessment data, accommodations, interventions).

Guardrails Engine: RBAC, field-level permissions, PII masking, parental consent receipts, audit trails. Sync & Freshness: event bus/CDC, backfills, idempotent jobs, conflict resolution, replay.

### Agent Enablement (Optional)

Student Tutor: age-appropriate Q&A grounded in curriculum content, standards, and accommodations via MCP. Teacher Assistant: lesson support, differentiation ideas, formative assessment hints, progress report drafts.

Admin Assistant: attendance patterns, intervention tracking, compliance roll-ups, "what's changed?" digests. Model Hub: OpenAI, Gemini, Anthropic, Llama, or local/NPU — hot-swappable per policy/cost.

### Workflow Automation (Partner Districts)

Proactive nudges (attendance alerts, grade drops), intervention routing, parent communication drafts. Content pipelines (ingest → chunk → cite), formative assessment generation with teacher review. Approval gates for instructional control (human-in-the-loop).

## Deliverables You Keep (No Lock-In)

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MCP server source code for every connected district system

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Connector code & IaC (Terraform/K8s manifests) to deploy in your VPC/on-prem

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Data dictionaries, MCP tool schemas, and contract tests

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Policy configs (RBAC matrices, redaction rules, parental consent, retention/expiry)

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ETL/ELT jobs, sync runbooks, and observability dashboards

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Agent starter kits (prompts, MCP tool definitions, evaluation harnesses)

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Security & Compliance packet (threat model, MCP data flows, COPPA/CIPA/FERPA audit checklist)

## Engagement Model (Hours-Based, Transparent)

### Discovery & Design (1–3 weeks):

workshops, MCP architecture, system inventory, backlog, estimates

### MCP Server Sprints (2–6 weeks):

build and test MCP servers for each district system, memory layer, policy engine

### Pilot & Hardening (2–4 weeks):

limited schools or grade levels, telemetry, MCP performance tuning, handover

### Handoff or Co-Manage:

your team runs it; we stay on a light retainer if desired

### Billing:

hourly, ultra-competitive rates; weekly timesheets; milestone demos; you can pause/rescope anytime

## Security, Privacy, and Compliance

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All MCP servers run in your environment (AWS/Azure/GCP or on-prem), with your IAM/KMS

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COPPA/CIPA/FERPA support, state student privacy laws, least-privilege MCP access

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MCP-level data minimization, field-level masking, parental consent receipts, audit logs

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Age-appropriate content filters, safety controls, and replay evaluation for agents

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mTLS between agents and MCP servers; no raw data sent to external LLM providers

## Reference Architecture (MCP-Powered)

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MCP Server Layer → Typed connectors to SIS/LMS/Rostering/Assessments/Identity/Storage

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MCP Gateway → Authentication, rate limiting, request routing, and observability

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Event Bus + CDC → Reliable syncs, backfills, and change capture

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Student Memory Layer → Graph + vector store with MCP-aware, age-appropriate policy retrieval

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Policy/Guardrails Engine → RBAC, PII redaction, parental consent, content-safety filters

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Agent Interfaces → Tutor (student), Teacher Assistant (teacher), Admin Assistant (district staff)

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Observability → MCP request traces, latency metrics, cost monitors, evaluation harnesses

## Common Use Cases We Deliver

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"Single pane of glass" tutor with assignments, accommodations, and reading-level context — powered by MCP connections to SIS, LMS, and assessment systems

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Teacher assistant that drafts differentiated activities and triages parent questions (cited answers from MCP-connected curriculum materials)

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Admin assistant that surfaces attendance patterns and intervention needs with provenance via MCP queries across enrollment, grades, and behavior systems

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Cross-system automations: attendance triggers, grade-drop alerts, parent notification workflows — orchestrated through MCP tool chains

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Curriculum content ingestion pipelines with standards alignment and citations

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Multi-agent workflows where specialized agents collaborate through shared MCP servers — one agent handles tutoring, another handles intervention tracking, a third handles parent communication — all sharing the same secure data layer

## Why ibl.ai FDEs

### MCP-native architecture:

every integration we build follows the open MCP standard — no proprietary lock-in

### K-12 native:

OneRoster, Clever, SIS/LMS nuances, COPPA/CIPA/FERPA governance baked in

### Ownership by design:

you get the MCP server code, configs, and deployment scripts

### Model-agnostic and cost-aware:

MCP works with any LLM provider; swap models and optimize for accuracy and spend

### Speed + rigor:

we ship working MCP integrations quickly, with tests and runbooks

## Get Started

### Architecture Review (hours):

map systems, goals, risks, and design your MCP server topology

### Fixed-Scope Pilot (optional):

cap hours for MCP servers covering a specific school or grade band

### Ongoing Hours (as needed):

new MCP servers, additional connectors, and workflow builds

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*[View on ibl.ai](https://ibl.ai/service/data-engineering/k12)*