Role in the Ecosystem
Enhanced Context MCP is the AI Intelligence Layer of the VISHKAR ecosystem. It provides development methodology guidance, specialized agent profiles, and context-aware tooling for autonomous software development.
- ✓ 17-Step Enhanced SDLC with 4-Angle Internal Review
- ✓ 38 specialized VISHKAR agents (32 technical + 6 domain)
- ✓ Contextual agent selection by file patterns
- ✓ POC building methodology (QIP framework)
- ✓ Intent-based context loading
Quick Example
# Get SDLC guidance
curl -X POST \
https://enhanced-context-mcp.vercel.app/api/mcp \
-H "Content-Type: application/json" \
-H "X-API-Key: pk_your_key" \
-d '{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "get_sdlc_guidance",
"arguments": {"section": "overview"}
},
"id": 1
}'Ecosystem Architecture
┌─────────────────────────────────────────────────────────────────┐
│ VISHKAR AI Agent │
└──────────────────────────────┬──────────────────────────────────┘
│
┌──────────────────┴──────────────────┐
▼ ▼
┌───────────────────────┐ ┌───────────────────────┐
│ PROJECT REGISTRY │◄────────────►│ ENHANCED CONTEXT MCP │ ◄── YOU ARE HERE
│ (Entry Point) │ │ (AI Intelligence) │
└───────────────────────┘ └───────────┬───────────┘
│
┌────────────────────────────────────┼────────────────┐
▼ ▼ ▼
┌───────────────────────┐ ┌───────────────────────┐ ┌───────────────────┐
│ JIRA MCP │ │ CONFLUENCE MCP │ │ STORYCRAFTER MCP │
│ (41 Tools) │ │ (32 Tools) │ │ (4 Tools) │
└───────────────────────┘ └───────────────────────┘ └───────────────────┘
Enhanced Context MCP Provides:
• 17-Step SDLC Guidance → Development methodology for all MCPs
• 38 VISHKAR Agents → Specialized reviewers (architecture, security, etc.)
• Contextual Selection → "Give me the right architect" for any file
• POC Methodology → QIP framework for proof-of-concept sitesKey Capabilities
17-Step Enhanced SDLC
Autonomous development lifecycle with 7 phases including 4-Angle Internal Review.
Phases:
- 1. Task Selection
- 2. Implementation
- 3-6. 4-Angle Internal Review (Arch, Security, Quality, Tech-Stack)
- 7. Feedback Integration
- 8-11. Testing (Unit, Integration, E2E, Security)
- 12-14. PR & Review
- 15-17. Merge & Deploy
38 VISHKAR Agents
Specialized AI agents for every development discipline.
Agent Categories:
• Architecture Review
• Security Audit
• Frontend Dev
• Backend Engineer
• TypeScript Pro
• FastAPI Expert
• Terraform Specialist
• Test Automator
• E-Commerce Planning
• Healthcare Domain
• FinTech Compliance
• + 27 more...
Contextual Agent Selection
"Give me the right architect" - automatic agent matching based on file patterns.
get_contextual_agent({
file_paths: [
"backend/src/api.py",
"frontend/Button.tsx",
"terraform/main.tf"
]
})
→ Returns: fastapi-pro, frontend-dev,
terraform-specialistPOC Building Guide
QIP methodology for interactive proof-of-concept sites.
6-Section Framework:
- 1. Questions (Clarifying Questions)
- 2. Architecture (System Design)
- 3. Delivery (Timeline & WBS)
- 4. Risks (Risk Analysis)
- 5. North Star (Vision & Goals)
- 6. Demo (Interactive Prototype)
Available Tools
| Tool | Description |
|---|---|
| get_started | Onboarding and ecosystem overview |
| get_sdlc_guidance | 17-Step SDLC with agent mappings and quality gates |
| get_contextual_agent | Match files to specialist agents automatically |
| load_enhanced_context | Intent-based context loading with smart selection |
| list_vishkar_agents | List all 38 VISHKAR agents with filtering |
| load_vishkar_agent | Load complete agent profile with examples |
| get_poc_building_guide | QIP methodology for POC sites |
| validate_vishkar_agent_profile | Validate agent profile format |
| refresh_agent_cache | Clear and reload agent profiles |
| update_agent | Update agent configurations with learning |
Usage Examples
Get 17-Step SDLC Overview
POST /api/mcp
X-API-Key: pk_your_key
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "get_sdlc_guidance",
"arguments": { "section": "overview" }
},
"id": 1
}List VISHKAR Agents by Type
POST /api/mcp
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "list_vishkar_agents",
"arguments": { "agent_type": "domain_expert" }
},
"id": 1
}Get Right Architect for Files
POST /api/mcp
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "get_contextual_agent",
"arguments": {
"file_paths": ["backend/api/routes.py", "frontend/src/App.tsx"]
}
},
"id": 1
}