How to Use MCP to Assess, Not Price, Your Company's Data Assets

The Model Context Protocol (MCP) can help assess the attributes of a business dataset that contribute to its potential licensing value. By providing a live, queryable interface to operational systems, MCP allows advisors and owners to evaluate data quality, structure, history, and provenance. However, MCP is a tool for evaluation and readiness assessment; it does not calculate a financial value, imply ownership or licensing rights, or guarantee buyer interest, which ultimately determines the market price.

The business problem: valuing intangible data assets

Most established companies sit on years of valuable, proprietary operational data locked away in ERP, CRM, and other line-of-business systems. This data—chronicling transactions, customer interactions, supply chain movements, and more—is a potentially significant intangible asset. Unlike tangible assets, however, its value is not recorded on a balance sheet and is difficult to quantify.

Traditional methods for assessing this data are often slow, expensive, and incomplete. Creating a static data dump for evaluation requires IT resources, and the data becomes stale the moment it is exported. Furthermore, a simple export loses crucial context about how the data is structured, its historical depth, and its auditable link back to the source transaction. This makes it challenging for advisors to gauge whether a client's data might be suitable for licensing to AI developers.

How MCP assists in data valuation readiness

MCP provides a standardized way for AI models to interact with business applications, acting as a live window into a company's data. This capability is uniquely suited for assessing a dataset's characteristics without needing a one-off data extraction project. It helps measure the factors that AI labs and data buyers look for.

Here is how MCP helps in the assessment process:

• Live Access and Freshness: Instead of working with a stale data export, MCP allows for live queries against the source system. This demonstrates the data's refresh rate (e.g., real-time, daily), a key indicator of value.
• Structure and Schema: An MCP server exposes the data's structure. An advisor can ask questions in natural language like, "What fields are in a customer record?" or "Show me the columns in the sales order table." This reveals whether the data is well-organized and relational or consists of unstructured, less-valuable notes.
• Historical Depth: An AI model connected via MCP can query the date range of records. Answering "What is the date of the first and last sales order?" instantly establishes the dataset's historical depth, a critical factor for training robust models.
• Data Provenance: High-quality datasets for AI require clear data provenance. Because MCP queries data from the system of record, it helps demonstrate that the data is not synthetic or scraped but is the auditable output of real business operations.

Using MCP for assessment is about understanding the raw material. It does not grant data licensing rights; that is a separate business and legal process that must be authorized by the company's decision-makers.

Illustrative example: assessing an e-commerce dataset for licensing

A fractional CFO advises a mid-market e-commerce company that has been operating for 12 years. The company uses NetSuite for its ERP and HubSpot for its CRM. The advisor suspects the historical, anonymized transaction and product data could be valuable for AI training and wants to assess its potential before presenting the idea to the CEO.

The company's IT team sets up a read-only MCP server connected to NetSuite and HubSpot. The advisor, using an AI assistant with access to this MCP server, runs a series of queries to populate a readiness rubric:

1. To check history and volume: `"Count the total number of line items from sales orders, grouped by year, for all years."`
2. To check structure: `"Show the available fields for a product record in NetSuite."`
3. To check granularity and anonymization potential: `"List five sample order line items from last quarter. Show product ID, quantity, price, and order date. Do not show any customer-identifying information."`
4. To check uniqueness: `"What custom fields exist on the HubSpot company record?"`

The responses allow the advisor to build a concrete profile of the dataset, noting its 12-year history, millions of transaction records, well-structured product tables, and specific custom fields capturing proprietary information. This forms the basis of an internal discussion about pursuing data licensing, backed by evidence directly from the source systems.

Data valuation readiness rubric

This rubric can help you assess the characteristics of a company's operational dataset. A higher score does not promise a specific valuation but indicates stronger potential alignment with the needs of AI labs and data buyers. It is a starting point for building an operational data inventory.

Prerequisites and limitations

While MCP is a powerful tool for assessment, its role and limitations must be clearly understood.

• Access is not ownership: MCP provides a technical means of access. It does not establish or transfer any ownership, copyright, or right to sell or license the data. Only an authorized decision-maker at the company can grant that permission.
• No valuation formula: There is no algorithm that can turn data characteristics into a dollar amount. This rubric assesses readiness and potential. The final valuation is set by the market—what a willing buyer will pay for a specific use case.
• Buyer demand is key: A technically perfect dataset has zero commercial value if no one needs it. The ultimate test is whether the data can help an AI lab solve a specific, high-value problem.
• PII and confidentiality: MCP respects the permissions of the underlying system, but this is not a substitute for a data privacy review. Before any data can be licensed, it must undergo a rigorous process to anonymize, aggregate, or remove all personally identifiable information (PII) and business-sensitive secrets.
• Third-party data: A company cannot license data it does not own. Data purchased from third-party providers (e.g., market research, financial data terminals) and accessed via MCP cannot be re-licensed to others. Read more on keeping internal and licensed data separate.
• No guarantees: An assessment using MCP, however positive, does not guarantee that SourceX or any other entity will find a buyer for the data, nor does it guarantee any level of revenue.

Questions to ask your software provider or implementation team

1. What specific datasets, tables, and fields can your MCP server connector access in our key systems (e.g., ERP, CRM)?
2. Does the MCP connector enforce read-only access to prevent any accidental modification of source records?
3. How does the MCP server expose the data schema or structure for an external AI model to understand?
4. What authentication method (e.g., OAuth, API keys, service accounts) does the MCP server use to connect to our source systems?
5. Can we configure the MCP server to exclude or mask sensitive fields by default before they are ever exposed to a connected AI model?
6. What level of audit logging is available to track which users or models queried what data and when?
7. How does the server handle queries that require joining data across different systems, such as in a CRM-to-ERP reconciliation?

Next step with SourceX

Assessing a dataset's technical and structural readiness is the first step. The next is to determine if a company's data profile aligns with what AI labs and data buyers are currently seeking. SourceX helps bridge this gap by evaluating company data for fit and managing the licensing process on behalf of the supplier.

For partners at private equity firms, M&A advisors, or CFO firms who have permission from their portfolio companies or clients, the Company Fit Checker is a great starting point for a single company. To screen several authorized companies at once, you can use our Portfolio Data Opportunity Scanner.

When an introduction you make through the SourceX partner program leads to a paid data license, you earn 25% of the platform fees SourceX collects, up to $100,000 per referred company. This reward is your share and is separate from the supplier company's own licensing proceeds.

Related MCP guides

• MCP Access vs. Data Licensing Rights: What Advisors Must Know
• Creating an MCP-Ready Operational Data Inventory
• MCP and Data Provenance: A Guide to Tracing Business Data Sources
• MCP and Portfolio Company Data Monetization: Where to Start
• All MCP resources

Sources

• Intralinks confidential deal data (Current guide)
• OWASP MCP security cheat sheet (Current security guidance)

Vendor capabilities change. Check current official documentation before relying on any product detail.