Skip to main content
To deliver accurate marketing analysis, AI agents require explicit context regarding underlying data schemas, supported metrics, and real-world business events. toorow provides a dual-layer context engine for agents.

​
1. Data Schema & Capability Context

AI agents must not guess column names or metrics. Before attempting complex queries or constructing report requests, agents inspect source capabilities using the core tool get_source_capabilities(project_id, connection_ref_id).

​
Dynamic Metadata Discovery

The capability descriptor returns:
  • Available Fields: Exact physical source field IDs (field_id), canonical target names (canonical_target), data types, and semantic hints (traffic, conversion, revenue).
  • Report Profiles: Supported grains (supported_grains), supported filter operators, row limits, and cadence bounds.
  • Constraint Repairs: If a query combination violates source bounds, toorow returns deterministic repair actions (unknown_report_field, unsupported_grain, unsupported_cadence).

​
2. Business Context Events (context_events)

Marketing metric spikes or dips rarely occur in isolation. toorow allows teams and agents to register real-world Context Events:

​
Non-Causal Candidate Context (AD-9)

When an agent analyzes an anomaly on a given date (e.g. a 25% drop in conversions), toorow correlates performance shifts against registered context events.

​
Next Steps & Cross-References

Agent Tools Reference

Inspect FastMCP tool parameters and execution schemas.

Agent Rules

Review system instructions for LLM context handling.

MCP Host Integration

Learn how external agents query context over FastMCP.

Use Cases Library

Explore real-world context event scenarios in MCP App widgets.