The Context Engine MCP is a standalone developer tool that exposes codebase intelligence via the Model Context Protocol. For the Context Engine as the core technology layer of the Qodo platform, see Understanding the Context Engine.
All three tools accept a
repositories parameter to scope the search and, where applicable, a session_id parameter to maintain context across follow-up calls.
deep_research
Thedeep_research endpoint is an intelligent code analysis agent that goes beyond simple search to provide comprehensive understanding of your codebase. Think of it as having a senior architect who has thoroughly studied every line of your code and can answer complex questions about architecture, patterns, and implementation strategies.
Key features
- Code understanding: Comprehends code logic, architecture, and design patterns
- Cross-repository analysis: Analyzes relationships between different parts of your codebase
- Implementation planning: Helps plan new features based on existing code patterns
- Best practice recommendations: Suggests improvements based on codebase analysis
- Architecture insights: Provides high-level understanding of system design
Request format
Parameters
Recommended query strategies
- Be specific and detailed. “How does user authentication work across our microservices?” provides richer insights than general queries.
- Include context about your goals. Mentioning why you need the information helps Deep Research tailor its analysis.
- Leverage
session_idfor complex investigations. Build on previous queries to dive deeper into specific areas. - Specify repositories for focused analysis. When you know which repos are relevant, include them for more targeted results.
- Ask “why” and “how” questions. Deep Research excels at explaining design decisions and implementation reasoning.
- Simple keyword searches: use
get_contextinstead - Asking the agent to modify your code:
deep_researchanalyzes and suggests code, it will not modify files - Questions about external services not in your codebase
- Real-time data: it analyzes code structure, not runtime behavior
Usage patterns
Pattern 1: Architecture discovery When to use: Understanding how your system works.context_ask
Thecontext_ask endpoint is an intelligent codebase question-answering system that combines semantic search with expert-level code analysis. Think of it as having a principal software engineer who instantly understands your entire codebase and can provide detailed, evidence-based answers with actual code snippets and precise implementation details.
Unlike traditional documentation or search tools, context_ask:
- Provides evidence-based answers with actual code snippets and line numbers
- Combines semantic search with graph-based repository knowledge
- Analyzes code relationships and dependencies across your entire codebase
- Delivers expert-level explanations with complete implementation details
- Shows actual code proof for every claim, never just descriptions
Request format
Parameters
Response format
Recommended query strategies
- Ask specific technical questions. “How does error handling work in the payment processing module?” rather than “Tell me about errors.”
- Request complete implementations. Ask for end-to-end flows, not just isolated functions.
- Focus on architectural patterns. Questions about how components interact yield comprehensive answers.
- Specify scope with repositories. Target relevant codebases for focused, accurate responses.
- Use session continuity. Maintain
session_idfor follow-up questions on related topics.
Usage examples
Understanding authentication flowget_context
Theget_context tool performs semantic search across one or more codebases to find relevant code snippets.
Key features
- Semantic search: Uses vector embeddings to find conceptually similar code, not just keyword matches
- Multi-repository support: Search across multiple repositories simultaneously
- Language filtering: Filter results by programming language (Python, JavaScript, TypeScript, etc.)
- Intelligent ranking: Returns results ranked by relevance with configurable result limits