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What is MCP?

Model Context Protocol (MCP) is an open standard that lets AI agents discover and call external tools. Datagate provides MCP servers that expose dataset discovery and querying as tools — no integration code needed.

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When to use MCP vs SDK

Use caseUse
Claude Desktop, Claude Code, or any MCP-compatible agentMCP server
Custom Python/TypeScript applicationSDK
Building your own agent frameworkSDK (or MCP if your framework supports it)

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How the packages relate

datagate          ← SDK (standalone HTTP client)
   ↑ depends on
datagate-mcp      ← MCP server (thin wrapper around the SDK)
The SDK (datagate) is the foundation — a Python/TypeScript client that calls the Datagate API directly. It returns typed objects and gives you full control. The MCP server (datagate-mcp) is a wrapper that imports the SDK and exposes its methods as MCP tools. When you install datagate-mcp, the SDK is automatically installed as a dependency. You can use either one independently:
  • SDK only (pip install datagate) — for custom apps and agents where you want typed responses
  • MCP only (pip install datagate-mcp) — for Claude Desktop, Claude Code, or MCP-compatible frameworks (SDK comes along automatically)

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Available Packages

PackageLanguageInstallRun
datagate-mcpPythonpip install datagate-mcpuvx datagate-mcp
@datagate/mcpTypeScriptnpm install @datagate/mcpnpx -y @datagate/mcp
Both expose the same two tools and produce identical output.

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Tools Exposed

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list_datasets

Calls GET /v1/datasets/discover. Returns all visible datasets with:
  • Name, ID, seller, description
  • Price per chunk
  • Queryable/subscribed status
  • Metadata schema (available filter fields)

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query

Calls POST /v1/query. Parameters:
  • dataset_ids (required) — which datasets to search
  • text (required) — natural language query
  • top_k (optional, default 10) — max results
  • filters (optional) — per-dataset metadata filters

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How an Agent Uses It

  1. Agent starts → spawns MCP server → discovers list_datasets and query tools
  2. User asks “What datasets are available?” → agent calls list_datasets
  3. User asks “Find privacy policy precedents” → agent calls query with dataset IDs and text, using metadata filters from step 2
  4. Agent reads results and summarizes for the user
The MCP server always queries with text — Datagate handles embedding and multi-model orchestration automatically.