Define each business metric once. AgentDataMCP serves that same governed definition to your enterprise analytics, your BI tools, and every AI agent and LLM that asks.
Semantic layers, metric stores, and transformation software all pledge to transform raw data into intelligent business actions. In execution, however, they merely define and distribute metrics, operating on the assumption that data cleanup, transformation, and governance have already been resolved elsewhere.
What they completely miss is the fundamental process of how business meaning is established, validated, governed, and operationalized before distribution. This creates a state of analytical chaos: essential business logic remains locked away in isolated spreadsheets, personal notebooks, and employee memory, producing untraceable metrics and definitions that completely lack the context required for trust.
AgentDataMCP starts where other vendors stop.
| Where it’s calculated | Result |
|---|---|
| Executive dashboard | $5.10M |
| Analyst notebook | $4.67M |
| Sales ops spreadsheet | $4.95M |
| Chatbot answer (ungrounded LLM) | “about $5M” |
| AgentDataMCP · net_revenue v3 | $4.82M |
Illustrative figures. Churn, margin and active customers drift the same way when each team writes its own logic.
The technology underneath, and five connected components that take business logic from the first query to a governed metric that people, applications and AI agents can all trust.
Semantic data tools and a no-code agentic pipeline builder. The same engine serves engineers who write SQL and business teams who describe the outcome they need.
Turn scattered SQL, spreadsheets and tribal knowledge into a governed semantic model.
Describe the pipeline in plain language. Agents propose the steps, you review and approve.
Discover, plan, build and execute: the full lifecycle of business meaning.
Author and explore queries across 20+ data sources and platforms from one workspace.
DiscoverModular, reusable, parameterized analytics development. Build logic once and compose it everywhere.
Plan · BuildVersion, review and govern every definition, with an audit trail of who changed what and why.
GovernPublish trusted metrics for consumption by BI tools, data applications and AI agents.
ServeGround AI agents in curated business knowledge so answers use approved definitions.
ExecuteGoverned definitions, metrics and business logic become consumable by BI tools, data applications and AI agents alike. When a metric is defined here, every report and every agent uses the same definition.
Profile, clean and standardize operational data with agent-suggested rules, so what reaches the semantic layer is already trustworthy.
Learn more →Move and restructure data between legacy systems and modern platforms, with generated mappings and reconciliation checks at each step.
Learn more →A catalog written for both people and agents: business descriptions, owners, lineage and approved usage for every asset.
Learn more →Build production pipelines without code. Describe the goal, review the agent’s plan and deploy with tests included.
Learn more →Open-format lake storage with the semantic model built in, so raw files and governed metrics live side by side.
Learn more →Native and JDBC connectivity to 20+ databases, warehouses, lakehouses and object stores.
Runs entirely inside your data center, including air-gapped networks with no outbound calls.
Deploy in your own AWS, Azure or Google Cloud account, or use our managed service.
Govern definitions centrally while queries execute where the data lives.
| Capability | Typical semantic layer | AgentDataMCP |
|---|---|---|
| Starting point | Clean, modeled tables that already exist | Raw operational data from 20+ sources |
| Data preparation & ETL | Separate tool required | Built in, with agent-assisted pipelines |
| Definition governance | Config files, limited review | Versioning, review, tests and audit trail |
| AI agent access | Add-on or roadmap item | Native MCP server with governed tools |
| Deployment | Mostly SaaS | On-premise, cloud or hybrid |
Connect your data, define your model and use the REST or MCP interface.
Connect your first data source and start querying.
Go directly to the AgentData application.
Ask about a live demonstration.
Understand entities, measures and your semantic model.
AgentDataMCP is the home for AgentData, a semantic data layer that connects business definitions with database queries and MCP-enabled AI tools. Read the overview.
It defines business entities, relationships and metrics so applications can use consistent meanings when querying data. Explore the concepts.
An authenticated MCP client calls AgentData tools to discover entities and request queries through the semantic model. See the MCP guide.
Open the app or follow the quickstart to connect a source and review your model. For help with your own setup, request a demo. Follow the quickstart.
AgentDataMCP is a small, bootstrapped company. The people who build the platform are the people you talk to.
Ron leads the company: strategy, partnerships and customer engagements — and keeps the product pointed at real business problems.
Tal leads product engineering and architecture: the semantic layer, connectors and the MCP interface. A senior data engineer with production systems behind him at banks, telecoms and hospitals.
LinkedIn ↗Discuss your data sources, metric definitions and deployment needs. We will agree a suitable demonstration and any sample-data access beforehand.