data-platform-mcp

Bring your data platform to AI agents. An open, production-minded Model Context Protocol server that exposes metadata, data lineage, and metrics as first-class tools for LLM agents.

Agent  ──MCP──>  data-platform-mcp  ──adapter──>  your data platform
                                              (OpenLineage / DataHub / OpenMetadata / custom)

Why

Data agents today burn most of their reasoning budget on addressing — finding the right table, the right metric definition, the right upstream dependency — not on the analysis itself. The fix is not a smarter model; it is exposing your data platform's semantic inventory through a stable, tool-oriented interface.

data-platform-mcp packages the tool categories a data agent actually needs, so platform teams can mount their metadata/lineage/metrics estate onto any MCP-capable agent (Claude Code, Claude Desktop, Codex, custom orchestrators) in an afternoon instead of a quarter.

Design distilled from operating an enterprise-grade data MCP in production (six-figure monthly tool calls, dozens of daily active analyst users). This repo is a clean-room, vendor-neutral re-implementation — it contains no proprietary code.

Related MCP server: MetaGraph-MCP

Tool surface (v0 roadmap)

Design principles carried through every tool:

1. Progressive disclosure — return summaries first; detail on explicit drill-down. Never dump a 10k-table catalog into the context window.

2. Structured facts, not prose — tools return typed, bounded JSON. Interpretation is the agent's job.

3. Address-first — every entity carries a stable ID + a human term; term-to-ID resolution is a first-class operation, because term mismatch is the #1 failure mode of natural-language data access.

4. Refuse over guess — when no match is found, return near-misses and confidence, not a wrong answer.

Project layout

data-platform-mcp/
├── src/            # MCP server core (tool registry, auth, caching, paging)
├── adapters/       # one adapter per backend (openlineage, datahub, openmetadata, rest)
├── docs/           # tool specs, design notes, deployment guide
└── examples/       # wiring samples for common agent clients

Quickstart (v0.1, in-memory demo adapter)

git clone https://github.com/wang-zhenlei/data-platform-mcp
cd data-platform-mcp
pip install -e .
data-platform-mcp        # stdio MCP server with demo data (4 tables, lineage, 2 metrics)
pytest -q                # smoke tests

Then register with any MCP client (Claude Desktop / Claude Code):

{
  "mcpServers": {
    "data-platform": { "command": "data-platform-mcp" }
  }
}

Try asking your agent: "which tables feed the returns fact table?" — it should call search_tables → query_upstream and answer from the graph, not from a schema dump.

Status

v0.1 — server core + in-memory demo adapter, runnable end-to-end. The tool contracts in docs/tools.md are the source of truth; the demo adapter implements them so agents and client integrations can be built today while real adapters are in progress. Contributions welcome, especially adapters.

Roadmap

• v0.1 — MCP server core + in-memory demo adapter (runnable end-to-end)

• v0.2 — OpenLineage + OpenMetadata adapters

• v0.3 — metric semantics layer (term resolution, definition governance)

• v0.4 — row/column-level permission enforcement hooks

• v1.0 — battle-tested, documented, semver-stable tool contracts

License

MIT