---
title: "Sample MCP connector map for enterprise agents — Ariesnet"
description: "A sample of the interview's deliverable: the systems of record your AI agents read, the MCP connectors they need, schema disconnects, ranked fixes."
canonical: "https://ariesnet.com/resources/sample-schema-landscape-map"
last_updated: "2026-09-25T07:58:17.005Z"
---

Brief

# Schema Landscape Map — a sample (fictional insurer)

A sample of what the interview produces — every system, owner, figure and name in it is fictional. It shows the shape of the map: the schema landscape, what needs to plug in, the disconnect register, complaints and opportunities, stage placement, and remediation options ranked.

Published September 5, 2026

Sample — fictional insurer — produced from the interview script. Every company, system, person, figure and date in this document is fictional. It shows the shape of the map the interview produces; it is not a client's map.

The interview is thirty minutes on a prospect's schema landscape (optional parts can extend it to forty-five). Within two business days it ends in a document of this shape: the systems that hold structure and meaning, what needs to plug in, where two systems disagree and what that is costing, a directional stage placement, and the remediation options ranked, with the parts the prospect can do alone marked as such. The map is the prospect's whether or not they go further.

Do this yourself this week

- List every system your MCP agents read today, and the format each one holds its structure in.
- Mark the two that disagree on one business term — the term your agents get wrong most often.
- Name the owner of each of those two systems: the person who can change the definition.
- Pull last month's agent or integration incidents from your ticket system; mark each as wrong tool, wrong field, or other; write down the count of the first two and their ticket ids.
- Write the one definition you would freeze first, in a sentence, and who has to agree to it.

## 1. Landscape map

| System | What structure it holds | Format | Owner (role) | Read by agents? |
| --- | --- | --- | --- | --- |
| Policy admin (core) | Policy, coverage, renewal, product codes | SQL DDL | Policy Ops lead | Yes |
| CRM (Salesforce) | Customer, household, contact preferences | JSON Schema | CRM product owner | Yes |
| Warehouse (Snowflake) | Claims, premium, actuarial aggregates | SQL DDL | Data platform lead | Yes |
| Content CMS (Contentful) | Product copy, disclosure text, FAQs | JSON Schema | Marketing ops | No |
| Partner exchange | Binding, endorsement payloads | XSD | Integration architect | Yes |
| Internal wiki | Ad-hoc field notes, FIBO-derived ontology snippets | Wiki pages | Claims Ops and Legal both edit the FIBO-derived snippets; last silent overwrite: March. 3 agent answers used the overwritten FIBO sense in April; tickets INC-4412, INC-4489, INC-4510 (fictional). | No |

## 2. What needs to plug in

1. When a renewal quote is requested, the agent needs to resolve the insured party and active coverages across CRM and policy admin, so that the quote uses one identity and one product code set. Source: policy admin. Consumer: quote agent. Direction: pull, through connectors.
2. When a claims intake bot classifies a loss, the agent needs validated coverage semantics from the Core Model, so that routing matches the conformance contract. Source: warehouse and policy admin. Consumer: claims agent. Direction: pull, through the MCP endpoint.
3. When product copy changes in Contentful, downstream agents need a change log of term and disclosure updates, so that answers stay aligned with filed wording. Source: Contentful. Consumer: advisory agent. Direction: push on change.

## 3. Disconnect register

| Id | The two systems | The disagreement | Stated cost | Owner |
| --- | --- | --- | --- | --- |
| D1 | Salesforce and policy admin | 'customer' vs 'policyholder' vs 'insured'; household vs named insured | About 120 hours a quarter of reconciliation; 6 priority incidents; wrong insured identity produces 14 wrong-tool logs a week, counted from the platform incident log | No owner |
| D2 | Policy admin and partner XSD | Renewal date: anniversary vs billed-to vs effective | A partner launch blocked; 3 weeks of slip | Integration architect |
| D3 | Policy admin and Snowflake | Product code grain: plan vs coverage vs rider | About 40 hours a month of model rework | Data platform lead |
| D4 | Wiki ontology and the JSON Schemas | FIBO-derived terms not mirrored in live schemas | Agents invent synonyms; about 15 failed tool calls a week | No owner |

Costs are as the owners stated them in the interview and were not measured, with one exception: the 14 wrong-tool logs a week in D1 are counted from the platform's own incident log.

## 4. Complaints and opportunities

Complaint: quote agents fail the identity join on about 18 percent of multi-policy households, adding about 25 minutes of average handle time.

Opportunity: one governed Core Model, with connectors and an MCP endpoint, could reduce cross-system lookups on the quote path from five hops to one.

What would have to be true: a conformance contract, published schemas with a change log, and named owners for D1 to D4 before agent operations scale.

## 5. Stage placement

Sources and Structure are largely in place (SQL DDL, JSON Schema, XSD), but Semantics and Validation are uneven: the FIBO-derived ontology lives on the wiki and does not bind the live schemas. Retrieval and Agent Operations exist in pilots. A Learning Loop from failed tool calls back into the Core Model and its change log is not yet institutionalized.

This placement is directional, from a structured conversation. The Diagnostic Briefing places each stage on the L0 to L5 scale with the reasoning shown; the Core Model Blueprint measures it.

## 6. Remediation options

| Option | Fixes | Effort | Self-serve? | With help | Deliberately does not fix |
| --- | --- | --- | --- | --- | --- |
| Publish an identity and product-code Core Model slice; wire connectors for the quote path | D1, D3 | Weeks | No | Core Model Blueprint, Implementation | Partner renewal-date XSD; CMS change log |
| Stand up the MCP endpoint and a conformance contract for claims classification | D4 (partial) | Weeks | No | Implementation, Agent Context Cost & Failure Audit | Full FIBO alignment; Contentful sync |
| Self-serve disconnect register and owner RACI, from wiki to schema index | D1 to D4 (visibility) | Days | Yes | Diagnostic Briefing | The semantic merge itself |
| Self-serve change-log template for Contentful, feeding the agent context pack | Enables item 3 of section 2 | Days | Yes | Diagnostic Briefing | Policy and CRM identity model |
| Managed operations: scheduled failed-call review feeding Core Model updates | D4 | A quarter | No | Managed Operations | Legacy XSD partner freeze |

Ranked by impact over effort: the Core Model slice, then the MCP endpoint and conformance contract, then the disconnect register, then the change-log template, then managed operations. Connector availability is as labelled on coremodels.io/connectors on the day of the interview; the catalogue is the source of truth.

## 7. Not covered

- Regulatory filing systems, actuarial pricing engines, and third-party credit and identity bureaus.
- Runtime authorization, PII redaction policy, and multi-region schema promotion.

## 8. Reviewer

Reviewed by: name withheld in the sample, Ariesnet. Date: withheld in the sample.

Questions

## Answered directly

**What does the interview produce, and what does it cost?**

Thirty minutes on your schema landscape — optional parts can extend it to forty-five — and within two business days a written map: the systems that hold structure and meaning today, the connectors your agents need, where two systems disagree and what that is costing, and remediation options ranked, with the parts you can do yourself marked as such. The map is yours whether or not you go further, and there is no rung to buy first. A non-disclosure agreement is available on request before the interview.

**When two systems disagree on the same metric, how is it resolved and can I reproduce yesterday's answer?**

The disagreement is resolved as a mapping in the Core Model, approved by the owner of each schema, and recorded in the change log with who changed what and when. Agents read the model through the MCP endpoint, so an answer traces to the model version it was read from. Version history is a Team and Enterprise feature on coremodels.io; with it in place, yesterday's version is read back, and the answer with it.

Terms used

## Definitions

**Context supply chain**

The end-to-end path that sources, structures, validates, retrieves, and governs enterprise context so agents can act on material that is correct, current, and usable. The industry has a name for this work now — context engineering. The context supply chain is how we do it. See also: Maturity level, Retrieval quality

**Maturity level (L0 to L5)**

A six-point placement applied per stage: L0 Ad hoc, L1 Structured, L2 Semantic, L3 Validated, L4 Operational, L5 Self-improving. Stages are scored independently — a mature retrieval layer sitting on L0 sources is a common and diagnostic pattern. See also: Context supply chain

Related engagement

## Want this assessed against your own estate?

The Core Model Blueprint takes one named agent workflow through all seven stages with your systems and your content, and ends in a sequenced Core Model your team could execute without us.

[See the Blueprint](https://ariesnet.com/products/context-supply-chain-assessment)

Next step

## Find out what your context is actually costing you.

We work with enterprise AI and data leadership teams. Thirty minutes, and you keep the map whether or not you go further. Not ready to talk? Read the field guide instead.

[Request an interview](https://ariesnet.com/interview) [Get the field guide](https://ariesnet.com/field-guide)

What happens next

1. The interview. Thirty minutes on your schema landscape — optional parts can extend it to forty-five. You leave with a map of the connectors you need and the wiring it will take to reach one governed Core Model — yours either way.
2. A written scope. Fixed price, fixed dates, named deliverables. Published bands; your fee is fixed in the written scope before we start.
3. A rung on the ladder. We recommend starting at the two-week audit.

Ariesnet Inc

Incorporated 1997 · Texas, United States

ARAMAI is the product group of Ariesnet, Inc., a Texas corporation. CoreModels is its platform, as part of the Schematica suite of solutions. Ariesnet contracts, builds and integrates for clients, and operates; ARAMAI does the research and makes the software.

Contact

- [info@ariesnet.com](mailto:info@ariesnet.com)
- [+1 214-932-3900](tel:+12149323900)
- Texas, United States

Elsewhere

- [CoreModels ↗](https://coremodels.io/)
- [ARAMAI ↗](https://aramai.net/)

© 2026 Ariesnet Inc. All rights reserved. · CoreModels® is a registered trademark. ARAMAI™ and Schematica™ are trademarks. · Elements of CoreModels are patent pending.

## Sitemap

- [Sitemap in markdown](https://ariesnet.com/sitemap.md)