ActComing Soon
Give AI agents the context they need
An MCP Server that exposes deep codebase intelligence to AI coding tools — so generated code follows your patterns, respects your architecture, and doesn't break what already works.
AI coding tools are powerful — but blind.
Without deep codebase context, AI agents generate code that compiles but doesn't fit. Wrong patterns, missed dependencies, violated conventions — creating more review work, not less. Code Lexica's MCP Server gives agents the same deep understanding that powers our reports and chat.
Capabilities
What the MCP Server provides
- Context retrieval — Architecture summaries, module responsibilities, dependency constraints, "how we do X" patterns
- Graph-aware change planning — Impacted modules, relevant tests, schema dependencies, integration points
- Standards & guardrails — Org patterns, preferred libraries, error handling style, observability requirements
- Implementation guidance — Scaffolds matching existing layering and conventions
- Evidence-backed answers — Responses reference exact files and functions
- Policy-ready prompting — "AI-ready" context packs for downstream agents
mcp-agent-query
> "Given this ticket, what modules
> should I touch and what should
> I avoid?"
Implementing 'user notification
preferences' should follow the
existing pattern in
NotificationSettingsService.
Changes will impact:
• UserService (user prefs schema)
• NotificationService (delivery)
• PreferencesRepository (storage)
Avoid:
• LegacyNotifier (deprecated)
• EmailService (use EventBus)
Recommended tests:
handlers/tests/
userPreferences.test.ts
Context pack: 12 files, 3 patterns,
2 constraints attached.Built for teams using AI coding tools
Developers
Using Cursor, VS Code agents, Claude Code
Platform teams
Standardizing AI-assisted development
Tech leads
Enforcing architectural and coding standards
Consultancies
Delivering across multiple client codebases
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