A prompt management server that exposes CRUD operations for LLM prompts through MCP tools like add_prompt, get_prompt, and apply_template. It handles template variable substitution, supports multiple storage backends including DynamoDB and S3, and can run in stdio mode for Claude Desktop or HTTP mode for REST integrations. The cognitive architecture layers add context-aware prompt recommendations and cross-domain pattern matching. You'd reach for this when building applications that need centralized prompt storage, versioning, and intelligent template management across different AI workflows, especially if you're already using AWS infrastructure or need Stripe payment integration for prompt marketplace features.
Simple MCP server for managing AI prompts and agent configurations with direct claude CLI orchestration.
data/prompts/pnpm install
pnpm build
pnpm start
Add to ~/.claude/mcp.json:
{
"mcpServers": {
"prompts": {
"command": "node",
"args": ["/absolute/path/to/mcp-prompts/dist/mcp-server-standalone.js"],
"env": {
"PROMPTS_DIR": "/absolute/path/to/mcp-prompts/data/prompts"
}
}
}
}
Ask Claude:
Use the orchestrate script to analyze entire projects:
./scripts/orchestrate-project.sh ~/projects/mia analyze
./scripts/orchestrate-project.sh ~/projects/esp32-bpm-detector review
This automatically:
| Tool | Description |
|---|---|
list_prompts | Query prompts with filters (tags, search, category) |
get_prompt | Retrieve specific prompt with template expansion |
create_prompt | Add new prompt to repository |
update_prompt | Modify existing prompt |
delete_prompt | Remove prompt |
apply_template | Apply variables to template string |
get_stats | Repository statistics |
data/prompts/
├── main-agents/ # 7 project orchestration templates
│ ├── main_agent_python_backend.json
│ ├── main_agent_cpp_backend.json
│ ├── main_agent_android_app.json
│ ├── main_agent_embedded_iot.json
│ ├── main_agent_multiplatform_iot.json
│ └── ...
│
├── subagents/ # 19 specialized analysis agents
│ ├── explorer.json # Project discovery
│ ├── analyzer.json # Code analysis
│ ├── diagrammer.json # Diagram generation
│ ├── solid_analyzer.json # Code quality
│ └── ...
│
├── cognitive/ # 7-layer cognitive architecture
├── esp32/ # Embedded systems patterns
├── mcp-tools/ # MCP usage patterns
└── [domains]/ # Domain-specific knowledge
Simple and focused:
┌──────────────────────────────┐
│ MCP Server (stdio) │
│ ├── list_prompts │
│ ├── get_prompt │
│ ├── create_prompt │
│ └── ... │
├──────────────────────────────┤
│ File Storage │
│ └── data/prompts/*.json │
└──────────────────────────────┘
Orchestration:
orchestrate-project.sh
↓ loads prompts
↓ builds agent config
↓ calls claude CLI
Actual agent execution
pnpm install # Install dependencies
pnpm build # Build TypeScript
pnpm dev # Watch mode
pnpm test # Run tests
pnpm orchestrate # Test orchestration
For enterprise features (AWS, multi-tenant, payments), see archive/aws/README.md.
Most users don't need this - the local MCP server is sufficient.
MIT
io.github.ericm1018/skillfm-llm-cost-optimizer-openai-anthropic-usage
io.github.mikerawsonnz/llm-orchestration-agent
io.github.mikerawsonnz/authenticated-llm-agent
labforgedev/copilot-memory-mcp
csoai-org/agent-prompt-injection-firewall-mcp
io.github.mikerawsonnz/authenticated-multi-llm-agent