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Turn intent into trustworthy execution

Context-aware specs, impact analysis, codebase chat, and AI agent guardrails — grounded in your actual codebase. Stop the ping-pong between product and engineering.

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Custom Reports & Planning

Purpose-built analyses tailored to your specific initiative — migrations, feature planning, compliance mapping, and more.

Available Report Recipes

Codebase Migration Assessment
Custom Onboarding Document
New Feature Planning
Regulatory Compliance Mapping (GDPR, HIPAA, SOC 2)
Dependency Upgrade Impact
Module Extraction Analysis

“Migration from React 17 to 18 affects 142 components; 89% are compatible, 12 require Suspense boundary additions, and 3 rely on deprecated lifecycle methods.”

Custom Reports

Interactive Codebase Chat

Ask questions about your codebase and get instant, context-aware answers backed by deep code analysis. Every answer traces back to source — no hallucination.

What you can ask

“How does the authentication flow handle expired tokens?”
“Where is the logic that calculates shipping costs?”
“What components would be affected if I changed the Order schema?”
“Write a spec for a new endpoint following our existing patterns”

Codebase Chat

JIRA & Linear Integration

Enrich tickets directly in your backlog with context-aware specs, implementation plans, and AI-ready prompts.

What gets added to tickets

Context-aware PRD/spec with acceptance criteria
Technical implementation plan with affected modules
Impact & risk analysis with dependency mapping
Testing plan with suggested unit/integration/e2e tests
AI-ready prompts tailored for coding agents

Generate Spec

Click on any ticket to generate a structured PRD with technical plan and acceptance criteria.

Backlog Grooming

Auto-draft specs, effort estimates, and dependency analysis for upcoming sprint items.

Start Work

Get an AI-ready prompt pack with constraints, examples, and relevant files for coding agents.

Post-Merge

Automatically generate "what changed" notes and update architecture documentation.

Before

PLAT-1234.txt

// JIRA Ticket: PLAT-1234
// Status: Backlog

Title: "Improve checkout performance"

Description:
  "Checkout is slow. Please fix."

Acceptance Criteria:
  (none)

Estimate:
  (none)

After enrichment

PLAT-1234-enriched.json

// JIRA Ticket: PLAT-1234 (Enriched)
{
  "title": "Optimize checkout API latency",
  "impact_analysis": {
    "services": ["OrderService",
      "PaymentGateway", "InventoryCache"],
    "files_affected": 14,
    "risk": "medium"
  },
  "acceptance_criteria": [
    "P95 latency < 200ms (from 800ms)",
    "Zero increase in error rate",
    "Cart abandonment rate stable"
  ],
  "implementation_plan": [
    "Add Redis cache for inventory",
    "Parallelize payment + inventory",
    "Add connection pooling"
  ],
  "testing_plan": [
    "Load test: 1000 concurrent",
    "Integration: payment retry flow",
    "E2E: full checkout journey"
  ],
  "estimated_effort": "3-5 days",
  "ai_prompt_pack": "available"
}

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

Plan with confidence. Ship with clarity.

Turn product intent into implementation-ready work — grounded in your actual codebase.

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