Architecture diagram with concurrency capacity and bottleneck estimates from any codebase.
Workflow Generator MCP server exists for a simple reason — assistants are far more useful when they can act on Workflow Generator directly instead of describing what you should do. Architecture diagram with concurrency capacity and bottleneck estimates from any codebase.
Scan any project and generate WORKFLOW.html — a dark-mode visual system diagram showing every component, how they talk to each other, and where your throughput ceiling actually is.
Once Workflow Generator is connected, these are the calls the assistant has available:
generate_workflow — scans project, writes WORKFLOW.html, optionally opens in browseranalyze_workflow — returns structured JSON summary (no file written)Section — What you getCategory — ExamplesGateways — nginx, Caddy, Traefik (with rate limits + worker_connections)Databases — PostgreSQL, MySQL, MongoDB, SQLite, RedisQueues — Celery, BullMQ, Kafka, RabbitMQ, RQ, AWS SQSWorkers — --workers N (uvicorn/gunicorn), replicas: (docker-compose), PM2 instancesEvaluation — TruLens, RAGAS, LangSmithThe server ships on PyPI as workflow-generator-mcp, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
This sits in the database access group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Workflow Generator's toolset — generate_workflow, analyze_workflow, Section and 6 more — is a fair guide to whether it matches your workflow. It is maintained by askuma; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Workflow Generator's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| generate_workflow | scans project, writes WORKFLOW.html, optionally opens in browser |
| analyze_workflow | returns structured JSON summary (no file written) |
| Section | What you get |
| Category | Examples |
| Gateways | nginx, Caddy, Traefik (with rate limits + worker_connections) |
| Databases | PostgreSQL, MySQL, MongoDB, SQLite, Redis |
| Queues | Celery, BullMQ, Kafka, RabbitMQ, RQ, AWS SQS |
| Workers | --workers N (uvicorn/gunicorn), replicas: (docker-compose), PM2 instances |
| Evaluation | TruLens, RAGAS, LangSmith |
{
"mcpServers": {
"workflow-generator": {
"command": "uvx",
"args": ["workflow-generator-mcp"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
Read-only SQL access to Postgres — let your assistant inspect schemas and answer questions from real data.
Manage your whole Supabase project in conversation — database, auth, storage, Edge Functions and branches.
Query, modify and analyse local SQLite databases in conversation — the fastest way to chat with a data file.
Metabase ships its own MCP endpoint — search your BI content, build and run queries, and save questions and dashboards without leaving the chat.
Official MongoDB server covering data, schemas and Atlas management — from find queries to spinning up clusters.
Serverless Postgres with database branching — point your assistant at Neon and let it work on disposable copies.