MCP server

AgentData ships a built-in Model Context Protocol server so AI clients can query your data directly — no glue code. It's the same semantic model and the same privacy guarantees as the REST API: only model metadata and the question reach the LLM.

Endpoint

https://agentdata.mdm.biskilled.com/mcp/

Streamable HTTP, authenticated with your API key (or OAuth for web connectors).

The Query → Connect (MCP) screen gives you the server URL, lets you create scoped API keys, and shows ready-to-paste config for Claude, ChatGPT, Cursor and the CLI:

Connect a client

Claude Desktop / Claude Code — add to your MCP config:

{
  "mcpServers": {
    "agentdata": {
      "url": "https://agentdata.mdm.biskilled.com/mcp/",
      "headers": { "Authorization": "Bearer agentdata_sk_…" }
    }
  }
}

Web connectors (claude.ai, ChatGPT) — add the URL as a custom connector; they'll complete the OAuth flow automatically.

Tools

Tool	Arguments	Returns
list_entities	status (default confirmed)	Entities with their measures and dimensions
describe_entity	entity_name	Full detail: labels, role, measures, dimensions, attributes, bindings, sample queries
query_metric	entity, measures[], dimensions[], filters[], time_range, order[], limit	data, row_count, generated_sql, engine, chart_url
query_nl	question	data, row_count, generated_sql, notes, chart_url
list_saved_queries	—	Saved query templates
run_saved_query	name	Result of a saved query
list_flows / run_flow	name	List / run an ETL flow

A typical agent flow: call list_entities to see what's available, describe_entity to learn an entity's measures, then query_nl or query_metric to get the answer. These map directly to the REST endpoints in the API Reference.

A note on two MCP servers

AgentData's MCP server queries your data. It's different from AgentDesk's MCP server, which exposes a codebase's project memory to your AI coding tools. Same protocol, different job.