How Maguyva Works
Connect a GitHub repository and Maguyva turns it into several searchable views your agent can use on demand. Instead of dumping entire files into context, the model gets grounded access to symbols, structure, docs, dependencies, and ranked code fragments.
The Context Problem #
As your projects grow, AI agents struggle with:
- Context Window Limitations - Too much information overwhelms AI models
- Irrelevant Data - AI agents waste tokens on unnecessary information
- Poor Performance - Reduced accuracy and slower processing times
- Manual Management - Developers spend hours organizing context manually
- Higher Costs - Without context management, token costs increase with every query
The larger a project grows, the more noise enters the context window — and agent accuracy drops as a result.
Maguyva's Solution #
When you connect a repository, Maguyva runs cloud ingestion pipelines over code, docs, manifests, symbols, and dependency metadata. The goal is to build durable code memory once instead of making every agent run rediscover the repo from scratch.
Those pipelines produce several specialist data layers: exact text search, semantic retrieval, symbol and reference lookups, structural views, and dependency graph analysis. Different questions route to different layers.
Search combines 5 retrieval modes: semantic, AST, symbol, dependency, and text. Results are fused and ranked so important symbols surface before low-value helpers.
The private repository views sit alongside public reference data for supported languages and text-based technologies. Your agent reaches the whole system through MCP tools, which you can lean on heavily while planning and validation or use as spot checks during editing.
1. Repository Ingestion #
After GitHub connects, Maguyva scans the repository in the cloud and extracts the parts an agent actually needs to reason about:
Code Structure Analysis ░╔═╗░ ▓║◉║▓ ░╚═╝░
Dependency Resolution ╲▄█▄╱ ─◆●◆─ ╱▀█▀╲
- Documentation Integration - Pulls in repository docs and related reference material alongside the code
2. Specialist Retrieval Layers #
Instead of one generic index, Maguyva builds several retrieval surfaces that answer different questions quickly and with different evidence:
Context budget: Maguyva returns ranked code fragments with file paths and line numbers, rather than dumping entire files into context. Actual token reduction depends on repository size and query specificity.
Routing and Ranking #
Relevance Scoring ▄█▄ ║⊙⊙║ ╚═█═╝
- Noise Reduction - Strips boilerplate, redundant chunks, and low-value matches
- Context Prioritization - Orders results so the agent sees core symbols before edge-case helpers
Stored Representations #
Semantic Retrieval ┌─◈─┐ ║███║ ╚═══╝
- Symbol and Structural Views - Supports exact symbol lookup, references, and AST-shaped questions
Dependency Graph Analysis ▓█▓ ╔═●═╗ ╚═╤═╝
3. Model Context Protocol (MCP) Tool Layer #
Maguyva is delivered as a cloud-hosted MCP server. Your agent calls the tools for the lens it needs instead of relying on the model to remember your architecture from scratch. See the full MCP API reference for all available tools.
Benefits of MCP Compatibility #
- Use More or Less Tooling - Lean on Maguyva heavily while planning or just use it to sanity-check a risky change
- Better Plan Validation - Cross-check ideas against real symbols, references, and docs before editing
- Clearer Blast Radius - Dependency and graph tools show what might break if a symbol changes
- Forward-Compatible - Works with any client that supports the MCP standard
Technical Architecture #
Secure Processing Pipeline #
Security Features #
- Encryption in Transit and at Rest - Your code is encrypted in transit (TLS 1.2+) and at rest
- Secure Cloud Storage - Your code index is securely stored and encrypted
- Container Isolation - Each indexing job runs in a dedicated Cloudflare Container with its own compute resources
- Access Controls - Fine-grained permissions and audit logging
Performance Characteristics #
Performance varies by codebase size and query complexity. Maguyva focuses on delivering the most relevant context rather than dumping entire files. See compatibility for supported languages and clients.
What to Expect #
By intelligently filtering and organizing your codebase context, Maguyva helps AI agents work more efficiently:
- Better Planning Accuracy - Agents can validate an approach against the actual repository before they start coding
- Safer Refactors - Blast-radius and dependency checks reduce avoidable breakage
- Lower Token Waste - Only relevant fragments are returned instead of whole files
- Faster Repo Understanding - The model gets grounded answers quickly instead of rediscovering project structure every run
Results vary depending on your codebase size, complexity, and the types of queries your AI agent handles.
Keeping Your Index Current #
Automatic Updates #
- Repository Sync - Stays current with your latest code changes
- Incremental Processing - Only processes changed files for efficiency
- Branch-Aware Context - Maintains separate indexes per git branch so agents see the right context for each branch
Getting Started #
Ready to try it?
- Start free - No credit card required
- Connect your repository — link a GitHub repo in the Maguyva portal
- Point your agent at Maguyva - Use the MCP tools while planning, checking, or editing (see the installation guide)
Repository Management #
Connect your GitHub repositories through the Maguyva portal. The platform automatically re-indexes when your code changes, keeping the MCP tool layer current for each branch. The portal also provides basic API usage visibility, with more detailed analytics on the roadmap.