About FlowMCP
FlowMCP normalises hundreds of data sources into a searchable library of AI-ready tools — so your agents can search, combine, and answer without turning you into an integrations maintainer.
FlowMCP started as a way to attach multiple data sources to a single MCP server. Today it is a schema library — 373 production-ready definitions, 291 data sources, 1608 callable tools — all routed through one auditable engine. AI agents call FlowMCP, never the underlying APIs directly. Your API keys stay in your control.
What FlowMCP is — and isn’t
Section titled “What FlowMCP is — and isn’t”What it is
Section titled “What it is”| Aspect | Description |
|---|---|
| Primitive idea | A flow of data — from heterogeneous sources, through normalised schemas, into AI pipelines |
| Schema library | 373 schemas (v4 production), 291 data sources, 1608 tool routes |
| Primary use | CLI tool — no MCP client holds 1608 tools in context, the CLI loads them dynamically |
| Secondary use | MCP server mode still supported, but not the default |
| Key isolation | Keys live in FlowMCP, never in the AI context — the AI sees calls and answers, never credentials |
| Reverse search | Schemas register themselves in Shared Lists like “Ethereum Mainnet” or “Berlin”, so an AI can ask which schemas cover a given topic |
| v4 | Skills, Prefill, Selections, Output-Schema, Pipes |
| v4.1 | Add-on concept — external toolkits like gtfs-sqlite-toolkit extend FlowMCP |
| Data classes | Crypto (EVM, Solana, DeFi, Identity, NFT), Open Data (DE/EU), Weather/Geo, Web3 Social, News, Dev-Tools |
What it isn’t (anymore)
Section titled “What it isn’t (anymore)”| Misconception | Reality |
|---|---|
| ”An MCP server” | No — CLI-first. MCP server mode is optional. |
| ”A crypto library” | Started there, broadened with open data since October 2025. |
| ”An API wrapper collection” | No — the engine is central, schemas are thin declarations. One engine audit covers thousands of endpoints. |
| ”An API key manager” | Not primary. Key management is a side effect of separating AI from key. |
Why we built it
Section titled “Why we built it”The problem
Section titled “The problem”Open data exists in vast quantity — transit schedules, weather, government records, geodata, environment data. It is also scattered across hundreds of portals, in different formats, behind different access methods. For humans, finding the right data is tedious. For AI agents, it is nearly impossible — without prior preparation.
What FlowMCP does
Section titled “What FlowMCP does”FlowMCP makes this preparation once, for all:
- Open protocols instead of closed platforms. A failed implementation can be replaced without changing schemas or clients.
- One source = one answer. Many sources = a useful answer. Real questions need combinations.
- Energy efficiency. Without a schema, every AI re-reads API documentation per request — thousands of tokens, inconsistent results. A schema is a one-time investment: every AI then uses it efficiently. Across thousands of requests, ~10x energy savings.
- Security through transparency. Schemas are open source, auditable, verifiable.
How it looks
Section titled “How it looks”
Use cases
Section titled “Use cases”Two scenarios that show FlowMCP + AI in action — not how to build an agent, but how FlowMCP turns scattered data into one answer.
- Deep Research — A research tool or planning software connects an agent. The agent queries FlowMCP for relevant data sources across open data portals, government APIs, and crypto catalogs in seconds. Read full case →
- Mobility — Catching the Connection — A live trip planner combines static GTFS (via the
gtfs-sqlite-toolkitv4.1 add-on) with real-time delay data. One CLI call, one answer. Read full case →
Production posture
Section titled “Production posture”| Signal | Status |
|---|---|
| License | MIT — use, fork, distribute |
| Source | github.com/FlowMCP — active repositories |
| Test coverage | Per repo, published via Codecov |
| Spec version | v4 active, v4.1 add-on layer, v3 in archive |
| Schema lifecycle | Defined per provider (Lifecycle docs in spec) |
| Maintainer status | Visible — see Team for contact |
Trust signals
Section titled “Trust signals”- Open source from day one. Schemas, core, CLI — all MIT, all on GitHub.
- Audit one engine, cover thousands of endpoints. The engine is central; schemas are thin declarations.
- API keys stay with you. FlowMCP holds keys at runtime — the AI sees calls and answers, never credentials.
- Spec evolution is documented. v3 → v4 → v4.1 changes live in the CHANGELOG.
Community
Section titled “Community”FlowMCP is open from day one — every schema, every CLI feature, every spec rule lives on GitHub. The schema library grows through contributions.
- Contribute a schema — Open a PR in FlowMCP/flowmcp-schemas-public following the v4 spec. The CLI validates locally before submission.
- Report an issue or request a schema — github.com/FlowMCP — file in the repo that matches the area (core, CLI, schemas, docs).
- Roadmap — Now / Next / Later. Shows what is shipping now, what is queued, and what we have deliberately chosen not to build. The Roadmap is the contract — anything on it is committed, anything not on it is not silently in progress.