AI-powered coding tools such as Cursor, Claude Code, and Gemini Code Assist are quickly becoming standard in modern development workflows. They excel at generating boilerplate code and accelerating iteration; however, when building applications that depend on live enterprise data, these tools operate blindly.
Imagine your AI coding assistant is generating Salesforce queries, but it referenced a Customer_Segment__c field that doesn't exist in your org, filtered by an invalid Stage value, and attempted a JOIN between Opportunity and a custom object using the wrong foreign key. The code looked perfect and was generated quickly, but it did not work in testing. This is not an example of AI failing; it is an example of AI not having proper context.
Enterprise data environments are rarely generic. They include custom schemas, organization-specific fields, and undocumented relationships. Without direct access to this information, AI-generated code is forced to rely on assumptions, which leads to broken queries, invalid filters, and repeated rework.
CData Code Assist MCP addresses this challenge by connecting AI coding tools to live enterprise data sources during development. The Code Assist MCP add-on enables schema discovery and live querying directly within AI-powered editors, allowing the AI to understand your real data model, tables, columns, relationships, and data types, before generating application code.
At runtime, applications use standard CData Drivers and Connectors (ADO.NET, JDBC, Python, and more) for secure, governed access. Because the Code Assist MCP add-on and drivers share the same data model, queries tested during development behave identically in production.
Core advantage 1: Custom object & field discovery
The challenge
Every organization customizes its data sources. Salesforce orgs add custom objects (ending in __c). Dynamics 365 deployments extend standard entities. ERPs have custom modules. Databases have organization-specific tables and columns. APIs expose proprietary endpoints. No standard documentation covers these customizations; they exist only in your specific environment.
When using AI coding tools without the Code Assist MCP add-on, the AI can only reference generic documentation and training data. It has no way to know that your Salesforce org has a Use_Case__c object tracking data integration scenarios or that your Account object includes a custom Current_ARR__c field. The AI generates queries based on what it thinks should exist. When your schema differs, and it almost always does, the code fails with errors.
How CData Code Assist MCP solves this
The Code Assist MCP add-ons' getTables and getColumns tools let the AI explore your actual schema in real-time. The AI discovers every custom object, every custom field, their data types, constraints, and metadata, all through interactive schema discovery during your conversation.
When you ask for a query involving opportunities and accounts, the AI doesn't guess which fields exist. It queries the schema metadata first to discover your actual table and column structure. The AI sees your actual schema, custom objects and all, and generates code accordingly.
Impact
The AI assistant builds queries using your actual source schema, not generic examples. When asked "Show me high-value accounts with their current ARR," custom fields like Current_ARR__c and Customer_Tier__c appear in generated code because the AI discovered them through schema discovery, not because a developer remembered to mention them or looked them up in documentation.
Without the Code Assist MCP add-on, the same query would reference only standard fields, resulting in incomplete data and requiring manual rework. Instead, this results in hours of schema documentation and field reference maintenance being eliminated, and no more “unknown column” errors from AI-generated code.
Core advantage 2: Relationship-aware JOIN logic
The challenge
JOINs are one of the most common failure points in AI-generated SQL. Standard relationships between core objects (like Opportunity.AccountId → Account.Id in Salesforce) are documented and predictable. But custom objects introduce foreign keys that are impossible to infer without metadata access.
AI assistant tools guess based on naming conventions. When your organization uses non-standard field names or complex relationships, those guesses can fail. The result: SQL syntax errors, zero or wrong results from incorrect JOINs, and hours spent debugging relationship logic.
How CData Code Assist MCP solves this
The Code Assist MCP add-on exposes foreign key metadata directly to the AI through the tools getTables, getColumns, and getSchemas. By querying relationship information, the AI understands how objects connect, across both standard and custom entities. When the AI needs to JOIN custom objects, it queries the schema for foreign key references, discovering exactly which fields link objects together. The AI discovers relationship metadata, then generates correct JOIN syntax automatically.
Impact
Every JOIN uses the correct foreign key field name, and complex relationship patterns are handled properly on the first attempt. Consider this real example: when asked, "Show me opportunities in the Evaluating stage with their accounts, related use cases, and any competitors mentioned," the AI discovered that Use_Case__c.Opportunity__c and Competitor_Mention__c.Opportunity__c are the foreign keys linking back to Opportunity.Id, and that Competitor_Mention__c.Competitor__c links to the Competitor__c custom object. It then generated a query with proper INNER and LEFT JOINs, correctly handling the many-to-many relationship with two separate JOIN clauses through the junction object.
Without the Code Assist MCP add-on, the AI would guess field names based on naming conventions, which would produce SQL errors or return no results. Now, there are no more incorrect relationship errors, and developers save hours on manual schema inspection and trial-and-error JOIN debugging.
Core advantage 3: Valid filter values from live metadata
The challenge
Accurate queries require valid filter values. But in enterprise systems, valid values are environment-specific and change over time:
Which opportunity stages are active in your sales process? (Prospecting, Evaluating, Negotiating, or something else?)
Which record types exist for a given object?
Which picklist values are currently allowed for custom fields?
What product SKUs or category codes are valid in your ERP?
AI assistant tools can’t answer these questions without data access. They guess based on common patterns, training data, or outdated documentation. The query runs but returns zero results, and you spend time debugging why WHERE StageName = 'Evaluation' doesn't work when your organization uses 'Evaluating'.
How CData Code Assist MCP solves this
CData Drivers expose metadata tables that describe valid values, such as PickListValues, RecordType, Formulas, and similar system tables. With the Code Assist MCP add-on, the AI queries these metadata tables autonomously to discover current, valid values before building filters. Before generating a WHERE clause, it retrieves active values specific to your environment, then uses them correctly in code.
Impact
Filters use verified, environment-specific values that guarantee accurate results. When asked, "Show me opportunities currently in the evaluation stage," the AI first queried the PickListValues metadata table and discovered the actual active stage values in this specific Salesforce org.
Without the Code Assist MCP add-on, the AI guesses values like 'Evaluation' or 'Closed-Won' that are close but don't match actual values 100% of the time. Debugging failed or empty results require manually querying the picklist metadata or logging into Salesforce to inspect field definitions. Now, queries work out of the box, and developers no longer need to manually find valid filter options.
Core advantage 4: Interactive query building with live validation
The challenge
Traditional development requires writing driver code, compiling, running, and discovering SQL errors at runtime. Each test and error triggers another code to compile to test cycle. Complex queries involving multiple tables, aggregations, or specific filter conditions can require dozens of iterations.
Even with correct schema knowledge, you can't be certain a query works until you run it against real data. Does the JOIN return the expected number of rows? Do the filters produce meaningful results? Are NULL values handled appropriately? You write what you think is correct, but verification only comes after deployment and testing.
How CData Code Assist MCP solves this
With the Code Assist MCP add-on, the AI assistant executes queries against live data during development, inside the conversation. Queries are built incrementally, validated step by step, and refined based on actual results from your actual data sources.
You describe what you need in plain language, and the AI does the rest. Explores the schema, generates SQL, executes it against your live data source, evaluates the results, identifies issues, and iterates, all without leaving your editor or switching to a test environment.
Impact
Complex, multi-table queries are built and validated in minutes instead of hours, with zero runtime errors after deployment. An immediate feedback loop with the AI assistant replaces the tedious and time-consuming code to compile to test cycle.
Core advantage 5: AI-assisted data integration across systems
The challenge
Enterprise integration scenarios often require moving data between systems with fundamentally different schemas. The source system uses a normalized, relationship-heavy model like Salesforce, while the target system expects a simpler, flatter structure like ERPs. Manually mapping these systems can become extremely time-consuming as the number of systems grows. Developers are required to understand the source system's normalized structure, the target system's expected format, and build transformation queries that bridge that gap.
How CData Code Assist MCP solves this
When CData Code Assist MCP is connected to both the source system and target system, the AI explores both schemas and generates the mapping of the query automatically. The developer simply states the integration goal conversationally, and the AI handles the technical implementation.
And because CData supports 350+ data sources through drivers for databases, SaaS platforms, ERPs, CRMs, APIs, and more, this approach works across virtually any integration scenario.
Impact
Schema mapping and data flattening logic is generated in minutes, not hours or days. The queries are validated against live data before deployment, and custom fields are included automatically through schema discovery.
Without the Code Assist MCP add-on, each integration requires hours creating mapping spreadsheets, manual schema inspection in multiple systems, trial-and-error JOIN logic across different data models, and complete rework when schemas change. Developers using the add-on save themselves from hours of manual labor, and integrations can automatically adapt as systems evolve because AI rediscovers changes.
From AI-Assisted Development to Production-Ready Applications
CData Code Assist MCP transforms AI coding tools from generic code generators into schema-aware development assistants. Instead of producing best-guess SQL and hoping it works, AI tools gain real-time visibility into your actual enterprise data model during development.
Without CData Code Assist MCP | With CData Code Assist MCP |
AI uses generic documentation | AI explores your actual system schemas |
Custom objects/fields are invisible | Custom objects/fields are discovered and contextualized |
Relationships must be guessed | Foreign keys are read from metadata |
Valid values unknown until runtime | AI finds metadata tables autonomously |
Code-compile-test cycle for each fix | Queries validated before code generation |
Hours mapping complex schemas manually | AI builds multi-source queries conversationally in seconds |
Because both the Code Assist MCP add-on and CData Drivers share the same foundation, queries validated during AI-assisted development translate directly into production code. Schema names, relationship mappings, and filter values discovered and tested during design-time sessions work identically when implemented through CData Drivers (JDBC, ADO.NET, Python, ODBC) in your applications, because they were validated against the actual live system.
Get started with CData Code Assist MCP
Ready to eliminate guesswork from AI-generated code? Explore CData Code Assist MCP and discover how schema-aware AI assistance transforms your development workflow.
Contact our team today to learn how CData Code Assist MCP can streamline your integration workflows and accelerate your product roadmap.
Explore CData Drivers and Connectors
Get blazing-fast access to live data with seamless connectivity from your data sources to the tools you use every day. Our standards-based connectors make data integration effortless – just point, click, and go.
