get_project_context

Get context about this client's data: agent roster, call volume, date range, verticals (industry), interaction drivers (why customers call), paradigm distribution, signal catalog (with 90-day frequencies), guidance catalog, and any synthesized coaching knowledge. Call this FIRST to understand who this client is before answering questions.

Parameters

This tool takes no user-facing parameters; project_id is injected from auth.

Example call

from fastmcp import Client
from fastmcp.client.auth import BearerAuth

async with Client(
    "https://mcp.chordia.ai/mcp",
    auth=BearerAuth("YOUR_APPLICATION_KEY"),
) as client:
    result = await client.call_tool(
        "get_project_context",
        {},
    )
    print(result.content[0].text)

In Claude Desktop, Cursor, or any MCP-aware client the same call is issued by the LLM automatically once the server is configured — see integrations.

Result

Success responses are JSON of the shape below. Optional fields are omitted when the underlying data isn't present.

{
  "project_id": "...",
  "project_name": "Acme Compass",
  "vertical": "sales",
  "agents": [
    {"name": "Alpha Agent", "call_count": 86}
  ],
  "signal_frequencies": [
    {"signal_key": "empathy_acknowledged", "frequency": 612}
  ],
  "outcome_drivers": [
    {"signal_key": "next_steps_clear", "avg_lift_when_present": 0.31}
  ],
  "total_calls": 1284,
  "date_range": {"from": "2026-01-01", "to": "2026-05-12"}
}

Auth context

Every call receives the connection's project_id and access_scope automatically — the caller never passes them. See Authentication & RBAC for how scope narrows results.

Errors

All tools return JSON. Errors are wrapped in { "error": "..." }. The most common shapes are documented in Python integration → Error shape.