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MCPEngineeringOnboarding

Anonymized enterprise AI agent case study · Engineering productivity

MCP context for engineering across mobile and backend teams

Developers connected their coding tools to Kognita MCP servers so agents could answer with complete system context instead of crawling code blindly.

Shared

MCP context servers

Cross-team

mobile and backend understanding

Chunked

code and database knowledge

The challenge

Mobile developers needed backend logic, backend developers needed product and app behavior, and no one wanted every repo hosted locally just to answer a question.

Generic coding agents spent large context windows reading files without understanding call graphs, database relationships, or internal architecture rules.

A rotating team needed onboarding and delivery confidence without relying entirely on tribal knowledge.

What Kognita ran

Kognita chunked code by logical units, documented callers, callees, repositories, file locations, and database entities.

Database tables were documented as chunks with columns, relationships, indexes, size, and usage notes.

Embeddings and MCP tools exposed the right semantic slice to Cursor, Claude Code, Codex, and other agent surfaces.

Business outcome

Developers could build and debug with system-specific context in their existing tools.

Mobile and backend teams needed fewer sync meetings for basic cross-system understanding.

Simple tickets could move faster because the agent started with architecture, nomenclature, and internal standards already loaded.

Evidence basis

Built from a real enterprise deployment, anonymized for confidentiality.

The deck says engineers connect Cursor and Claude Code to the same MCP servers, and describes code, database, semantic meaning, embedding, and MCP exposure as the backend pipeline. Exact client names, market names, private system identifiers, internal ticket IDs, and sensitive implementation details have been removed.

Source reference: Slides 6, 15, and 17-20 from the internal case-study deck.