daco-framework

# DACO — Declarative Agent & MCP Orchestration [](https://github.com/contactjccoaching-wq/daco-framework) [](https://opensource.org/licenses/MIT) **One MCP endpoint to rule them all.**  > Instead of configuring N separate MCP servers in Claude Desktop, you configure one: DACO. It routes tool calls to the right backend, executes them in parallel when possible, and returns structured results. The pattern is backend-agnostic. You define backends as simple modules (name prefix → handler function). DACO handles routing, parallel execution, error recovery, and the full MCP protocol over HTTP. The LLM poses questions. DACO executes. --- ## How It Works ``` Claude Desktop / Any MCP Client | | (single MCP connection) v +---------+ | DACO | Cloudflare Worker | Router | MCP Streamable HTTP +----+----+ | +----+----+----+----+ | | | | | v v v v v Backend A B C D ... ``` Each backend is a JS module exporting: - `TOOLS` — array of MCP tool definitions - `callBackend(name, args, env)` — handler function DACO merges all tool lists, dispatches by name prefix, and adds meta-tools on top (`daco_execute_parallel`, `daco_list_backends`). ## Current Backends (reference implementation) This repo ships with 4 backends as a working example: | Backend | Prefix | What it does | |---------|--------|---| | Smart Rabbit | `smart_rabbit_*` | AI fitness program generation | | PubMed | `pubmed_*` | Scientific literature search (NCBI) | | Brave Search | `brave_*` | Real-time web search | | FitLexicon | `fitlexicon_*` | Exercise database (873 exercises, 8 languages) | **To adapt DACO to your own use case**, replace these with your own backends. The orchestration layer doesn't care what the backends do. ## Deploy ```bash npm install npx wrangler secret put BRAVE_API_KEY npx wrangler secret put RAPIDAPI_KEY npx wrangler deploy ``` ## Configure in Claude Desktop ```json { "mcpServers": { "daco": { "url": "https://your-worker.workers.dev/mcp", "transport": "http" } } } ``` Or via local proxy: ```json { "mcpServers": { "daco": { "command": "npx", "args": ["mcp-remote", "https://your-worker.workers.dev/mcp"] } } } ``` ## Adding a Backend 1. Create `backends/my-service.js`: ```javascript export const MY_SERVICE_TOOLS = [{ name: 'myservice_do_thing', description: 'Does the thing', inputSchema: { type: 'object', properties: { query: { type: 'string' } }, required: ['query'] } }]; export async function callMyService(name, args, env) { const res = await fetch('https://api.example.com/...', { ... }); return JSON.stringify(await res.json()); } ``` 2. Import in `worker.js`, add to `ALL_TOOLS`, add prefix routing in `dispatchTool()` That's it. No config files, no plugin system. Just functions. ## Parallel Execution `daco_execute_parallel` fires multiple tool calls simultaneously: ``` daco_execute_parallel([ { tool: "pubmed_search", arguments: { query: "hypertrophy" } }, { tool: "brave_search", arguments: { query: "gym prices 2026" } }, { tool: "myservice_do_thing", arguments: { query: "..." } } ]) → All results returned in a single response ``` ## Related Projects - [**immune**](https://github.com/contactjccoaching-wq/immune) — Adaptive memory system — learns patterns from every scan (+85% code quality) - [**chimera**](https://github.com/contactjccoaching-wq/chimera) — Bio-inspired 3-stage pipeline (Slime Mold → PRISM → Immune) - [**spinal-loop**](https://github.com/contactjccoaching-wq/spinal-loop) — Neuromuscular-inspired agent routing (cheap models first) - [**prism-framework**](https://github.com/contactjccoaching-wq/prism-framework) — Multi-agent synthesis via native LLM stochasticity - [**smartrabbit-mcp**](https://github.com/contactjccoaching-wq/smartrabbit-mcp) — AI workout generator MCP server ([smartrabbitfitness.com](https://www.smartrabbitfitness.com)) --- MIT License — by [Jacques Chauvin](https://github.com/contactjccoaching-wq)