Prompts

Brain's MCP server ships 5 canned prompts for the most common agent loops. Prompts are pre-templated invocations that combine a question, the right resources to read, and the expected response shape.

Property	Value
MCP method	prompts/get and prompts/list
Required scope	Same as the underlying read tools

Why Canned Prompts

Most external agents end up reinventing the same five questions in their first day of integration. Canned prompts give them a one-shot way to get a high-quality answer without designing the chain themselves.

Prompt	Question It Answers	Underlying Reads
wiki.question.cash_flow_summary	"What's our cash position right now and over the last 30 days?"	accounts, balances, transactions
wiki.question.bills_due	"What bills are coming due in the next N days, in priority order?"	obligations, counterparties
wiki.question.spending_change	"What changed in our spending versus the prior period?"	transactions, categories
wiki.question.invoice_status	"What invoices are outstanding, and which are overdue?"	invoices, transactions, counterparties
wiki.question.subscriptions	"What recurring subscriptions are we paying for, and which are unused?"	obligations, transactions, counterparties

Anatomy of a Prompt

A prompt is a structured object that tells the agent's LLM how to use Brain's MCP surface to answer a specific class of question.

{
  "name": "wiki.question.cash_flow_summary",
  "description": "Ask Brain to summarize cash flow over a period.",
  "arguments": [
    {
      "name": "period",
      "description": "A human-readable period, e.g. 'this month', 'Q1 2026', '2026-04'.",
      "required": true
    }
  ]
}

wiki.question.cash_flow_summary

Pulls all active accounts, fetches the latest balances, lists transactions in the period grouped by direction (inflow vs outflow), and returns a structured summary plus a narrative.

Typical inputs:

{ "period": "this month" }

Typical output sections:

Section	Content
Cash position	Sum of current_balance across active accounts
30-day inflows	Total inflows, top 5 sources
30-day outflows	Total outflows, top 5 destinations
Net change	Inflows minus outflows
Anomalies	Flagged transactions over the agent's heuristic threshold
Evidence	Ledger transaction ids cited

wiki.question.bills_due

Lists obligations with status in (upcoming, due, overdue) ordered by due_date, with priority hints based on amount, counterparty risk, and days-until-due.

Typical inputs:

{ "days": 14 }

Each entry includes the obligation_id, amount_due, due_date, counterparty.name, counterparty.verified_status, and a recommended action: pay_now, schedule, review, or escalate.

The recommendation is generated by the calling agent, not by Brain. Brain returns the structured facts; the agent's reasoning produces the priority order.

wiki.question.spending_change

Compares the given period against the prior comparable period and surfaces the categories with the largest delta.

Typical inputs:

{ "period": "2025-09" }

Returns categories sorted by absolute change, with citations to specific transactions and counterparties driving the change.

wiki.question.invoice_status

Reports the status of a specific invoice: whether it has been paid in full, partially, or not at all, with the linked transactions cited.

Typical inputs:

{ "invoice_number": "INV-1042" }

Aging buckets: current, 1-30 days, 31-60 days, 61-90 days, 90+ days. Each invoice includes linked_transaction_ids[] so the agent can verify partial payments.

wiki.question.subscriptions

Identifies recurring obligations and pairs them with usage signals where available.

This prompt takes no arguments.

Returns each subscription's counterparty, monthly_amount, start_date, last_charge, recurrence, and a freshness signal computed from related Raw evidence (e.g., when the agent has contributed usage transcripts via raw.contribute, those are surfaced here).

Listing and Getting Prompts

POST /v1/agents/mcp HTTP/1.1
{ "jsonrpc": "2.0", "id": 1, "method": "prompts/list" }

POST /v1/agents/mcp HTTP/1.1
{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "prompts/get",
  "params": {
    "name": "wiki.question.cash_flow_summary",
    "arguments": { "period": "this month" }
  }
}

The prompts/get response contains a messages[] array suitable for direct injection into an LLM's context window. The agent runtime can render the messages, execute the embedded tool calls (Brain returns them with the right URIs and arguments pre-filled), and produce the final answer.

Audit

Like tools and resources, every prompts/get invocation emits an agent.mcp.tool_called audit event with method: "prompts/get" and the prompt name plus arguments in inputs.

What's Next

🛠️ Tools

The 16 tools the prompts orchestrate.

Tools

📦 Resources

The 7 resource templates prompts can reference.

Resources