docs: add Ag Advisor grain-marketing prompt library
Example end-user prompts (planning + monitoring) for the downstream Drawbar Ag Advisor, each mapped to the ag-bids MCP/API tools that answer it, plus cross-cutting synthesis examples. Linked from the README. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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For the full per-release detail (signatures, conventions, example question → call
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mappings), see [CHANGELOG.md](CHANGELOG.md).
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Grain-marketing prompt library for the downstream Ag Advisor (planning +
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monitoring, with the tools each prompt uses):
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[examples/ag-advisor-prompts.md](examples/ag-advisor-prompts.md).
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# Ag Advisor — Example Prompts (Grain Marketing)
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Example end-user prompts for the **Drawbar Ag Advisor**, the AI agent that
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consumes the `ag-bids` MCP tools (and the underlying `ag-monitor` `/api/data/*`
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HTTP API). They're grouped into **Planning** (build a marketing strategy) and
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**Monitoring** (track prices and act day-to-day), with the tools each prompt
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leans on so the advisor knows which calls answer the question.
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Copy this into the Drawbar repo (e.g. `prompts/grain-marketing.md`) as a prompt
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library / eval set. Phrasing is intentionally farmer-natural — the advisor is
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expected to translate it into tool calls.
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## What the advisor can see
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| Capability | Tool(s) |
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|---|---|
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| Live local cash bids (per elevator, crop, delivery) | `latest_prices`, `best_local_bid` |
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| Cash price history + basis trend per elevator | `price_history` |
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| Basis movement (overview, then drill-down) | `basis_movement` → `basis_detail` |
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| CBOT futures price + change (since open, on day) + carry | `futures_quote`, `list_deliveries` |
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| National price *received by farmers* ($/bu) + MoM/YoY + seasonal | `price_trend`, `price_series` |
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| Input costs — diesel ($/gal) + fertilizer ($/ton) + change + seasonal | `input_cost_trend`, `input_cost_series` |
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| Local fertilizer/lime snapshot (DTN dealer feed) | `current_input_price`, `current_lime_price` |
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| What's covered / is the data fresh | `list_sources`, `list_commodities`, `list_deliveries`, `source_health` |
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| One-call morning brief | `todays_summary` |
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Conventions the advisor should keep in mind:
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- **Basis is the lever.** Positive basis move = cash strengthened vs futures;
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negative = weakened. `basis_movement` aggregates and can net divergent
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elevators to "flat" — follow up with `basis_detail` when it looks surprising.
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- **Cash = futures + basis.** A sell decision is really two decisions (futures
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level *and* basis), and the carry (`list_deliveries` + `futures_quote` across
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months) tells you whether the market pays you to store.
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- `price_trend`/`input_cost_trend` are the **macro/seasonal benchmark** — "is
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today cheap or dear vs history" — not a local bid. Pair them with
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`best_local_bid`/`latest_prices` for the actionable number.
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- Grains: `corn`, `soy`, `wheat`. `geo` for `price_trend` is `US` or a 2-letter
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state (e.g. `OH`, `IA`). Fertilizer `geo` is an AgTransport region (default
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`Cornbelt`).
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---
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## Planning — build a marketing strategy
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**1. Build a pre-harvest plan**
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> "I grow corn and soybeans in west-central Ohio. Help me build a pre-harvest
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> marketing plan for this year's corn crop."
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Advisor pulls: `price_trend("corn", geo="OH", years=10)` (where price sits vs
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norm) · `futures_quote("corn", delivery="Dec 2026")` (new-crop level) ·
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`basis_movement("corn")` (is basis a reason to wait or sell) ·
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`best_local_bid("corn")` (today's actionable cash).
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**2. Sell now or store?**
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> "I've got 20,000 bushels of soybeans in the bin. Should I sell now or hold into
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> winter?"
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`price_trend("soy", geo="OH")` (seasonal percentile — is now historically high or
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low) · `list_deliveries("soy")` + `futures_quote("soy", delivery=…)` across months
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(does the carry pay me to store) · `basis_movement("soy")` (is basis likely to
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improve) · `best_local_bid("soy")`.
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**3. Does the market pay me to store? (carry)**
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> "Does the futures carry justify storing corn until March instead of selling at
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> harvest?"
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`list_deliveries("corn")` then `futures_quote("corn", delivery=…)` for the nearby
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vs deferred months → compare the spread against storage cost.
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**4. Time a new-crop sale**
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> "Is now a good time to make a new-crop corn sale for fall delivery?"
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`price_trend("corn", years=10)` (seasonal context) · `futures_quote("corn",
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delivery="Dec 2026")` (change on day / since open) · `basis_movement("corn",
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delivery="Oct 2026")` · `best_local_bid("corn")`.
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**5. Seasonality of basis / price**
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> "Historically, what time of year is corn basis strongest around here, and when
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> does the cash price tend to peak?"
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`price_series("corn", geo="OH")` over multiple years (seasonal shape) ·
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`price_trend("corn", geo="OH")` (this month's percentile vs the same month over
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the last N years) · `price_history("corn", days=…)` for the recent basis arc.
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**6. Compare which crop to sell**
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> "Relative to their 10-year norms, is corn or soybeans the better one to be
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> selling right now?"
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`price_trend("corn", years=10)` vs `price_trend("soy", years=10)` — compare
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percentile-vs-normal and YoY, then `best_local_bid` for each.
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**7. Breakeven / cost side of the plan**
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> "How have my input costs moved this year — what does that do to my corn
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> breakeven?"
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`input_cost_trend("diesel")` · `input_cost_trend("urea")`,
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`input_cost_trend("anhydrous")`, `input_cost_trend("dap")`,
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`input_cost_trend("potash")` (all $/ton, Cornbelt) — latest + YoY change feed the
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cost side; combine with `price_trend("corn")` for the margin picture.
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**8. Prebuy fertilizer decision**
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> "Should I prebuy urea and anhydrous now or wait until spring?"
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`input_cost_trend("urea")` and `input_cost_trend("anhydrous")` (seasonal
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percentile + YoY — are they historically cheap) · `input_cost_series(…)` for the
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trend shape · `current_input_price()` for the latest local dealer quote.
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**9. Set price targets / a scale-up plan**
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> "Give me three corn price targets to scale out the rest of my old-crop bushels,
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> based on where price sits historically."
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`price_trend("corn", geo="OH", years=10)` (normal, range, percentile) +
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`futures_quote("corn")` + `basis_movement("corn")` → advisor proposes laddered
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targets above the seasonal normal.
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**10. Wheat pre-harvest plan**
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> "Build me a marketing plan for this year's wheat ahead of harvest."
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`price_trend("wheat", geo="OH")` · `futures_quote("wheat", delivery="Jul 2026")` ·
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`basis_movement("wheat")` · `best_local_bid("wheat")`.
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---
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## Monitoring — track prices and act
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**11. Morning brief**
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> "Give me my grain marketing brief for this morning."
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`todays_summary()` (one call) — then drill in where it flags something.
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**12. Best bid right now**
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> "Where's the best corn bid near me today?"
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`best_local_bid("corn")` (or `latest_prices("corn")` to see every elevator).
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**13. Did futures move today?**
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> "How far has corn moved today — off the open and on the day?"
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`futures_quote("corn")` → reports change since open and change on the day.
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**14. Basis check**
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> "How has corn basis moved over the last two weeks, and which elevator improved
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> the most?"
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`basis_movement("corn", days=14)` for the headline, then `basis_detail("corn",
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days=14)` for the per-elevator split.
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**15. Best home for a specific delivery**
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> "Which elevator is paying the most for soybeans for January delivery?"
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`latest_prices("soy", delivery="Jan 2026")` / `best_local_bid("soy")`.
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**16. Is cash near a recent high?**
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> "Is the local corn cash price near its high for the last month?"
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`price_history("corn", days=30)` (recent arc + basis) · `price_trend("corn",
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geo="OH")` (seasonal percentile for context).
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**17. Spread between cash and CBOT**
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> "Has my local basis widened or narrowed this month?"
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`basis_movement("corn", days=30)` → if it nets flat or odd, `basis_detail(…)`.
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**18. All elevators at a glance**
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> "Show me today's corn bids across all my elevators."
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`latest_prices("corn")` (snapshot table, all sources).
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**19. What can I price?**
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> "What delivery months can I price corn for right now?"
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`list_deliveries("corn")`.
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**20. Data freshness / trust check**
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> "Are any of my price feeds stale or failing?"
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`source_health()` (and `list_sources()` for the full roster + last-success
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times).
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**21. Fertilizer price watch**
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> "Are fertilizer prices climbing — should I be worried about next year's input
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> bill?"
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`input_cost_trend("urea")`, `input_cost_trend("dap")`,
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`input_cost_trend("potash")`, `input_cost_trend("anhydrous")` — latest $/ton +
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MoM/YoY + seasonal; `input_cost_series(…)` for the trend.
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**22. Diesel watch for fieldwork**
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> "What's diesel doing — anything I should factor into fall fieldwork costs?"
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`input_cost_trend("diesel")` (U.S. retail $/gal, week-over-week + YoY).
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---
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## Cross-cutting — how the advisor synthesizes a recommendation
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These show the *reasoning*, combining several tools into one answer.
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**A "should I sell today" call:**
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> "Should I sell corn today?"
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1. `best_local_bid("corn")` — the actual number on the table.
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2. `price_trend("corn", geo="OH", years=10)` — is that number historically high
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or low for this month (percentile vs normal)?
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3. `futures_quote("corn")` — is the board strong/weak today, and which way is it
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moving (since open / on day)?
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4. `basis_movement("corn")` — is basis strengthening (a reason to wait) or
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weakening (a reason to capture now)?
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5. Synthesis: "Cash is at the 78th percentile vs the last 10 Mays, board is up
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6¢ on the day, and basis has firmed 4¢ over two weeks but looks toppy — taking
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a portion here and leaving the rest for the carry is reasonable."
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**A "store vs sell" call** combines `list_deliveries` + `futures_quote` (carry) +
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`basis_movement` (expected basis gain) + `input_cost_trend("diesel")` and storage
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assumptions to weigh the cost of carry against the expected pickup.
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> Guidance for the advisor: always anchor a recommendation to a **live**
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> number (`best_local_bid`/`latest_prices`/`futures_quote`) and use
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> `price_trend`/`input_cost_trend` only for *context*. State the basis and
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> futures pieces separately so the user can act on whichever is actually driving
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> the call.
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