justin
a766756a05
Phase 2 — Chunking and indexing
- rag/chunk.py: replace template chunker with seed-variety-specific
chunks_from_variety(). One chunk per variety (varieties are small
and named-rating retrieval signal is best kept together). Output
is rebuilt deterministically from the sidecar JSON: every value is
verbatim from the source, only framing language ("Disease ratings
(1-9, 9=best):") is template glue. Anti-hallucination contract:
same sidecar in → same chunk out, never a fabricated rating.
Metadata flattened to Chroma-safe primitives (str/int/float/bool):
source, source_key, vendor, brand, crop, product_name,
product_id, source_url, rm (corn), mg (soy), wheat_class,
release_year, trait_codes_csv, rating_scale.
- rag/index.py: walks corpus/<source>/<source_key>.json sidecars
via the new chunker. Default PRODUCT_NAME=crop_seed so the
Chroma collection is crop_seed_docs.
- rag/bm25.py: filterable columns updated to seed-domain facets
(source/vendor/brand/crop/source_key) instead of the template's
version/platform/product.
Phase 3 — MCP server tools wired up
- search_docs: hybrid dense (Chroma) + BM25 (FTS5) retrieval with
RRF fusion. Optional filters: crop, brand, vendor, source.
Variety-code prefilter pins exact source_key / product_name /
hybrid_prefix matches at the top — dense embeddings have no
semantic neighbor for tokens like "DKC62-08RIB" and RRF can let
noise float to #1 without this pin. Each response carries the
variety's source URL inline so the agent can cite.
- get_page(source, source_key): emits a structured ratings header
(verbatim from sidecar, table per characteristics group, vendor
positioning, regional listings) followed by the raw indexed body.
This is the canonical fact-check surface.
- list_versions(): facet discovery — distinct sources, vendors,
brands, crops across the corpus.
- lookup_variety(source_key, source?): returns the raw sidecar JSON
for one variety. The agent should call this BEFORE quoting any
specific rating value to a farmer — guaranteed verbatim.
Smoke tests against 475 indexed Bayer varieties:
- list_versions returns 475 varieties, 1 source, 1 vendor, 3 brands,
3 crops with correct per-brand counts (288/102/85).
- Semantic ag queries find the right candidates: short-season
drought-tolerant corn → DKC44-97RIB at RM 94 (in 90-95 band);
SCN+MG3 soybean → Asgrow XF varieties with explicit SCN R3 ratings;
Phytophthora Rps3a soy → AG07XF4 (right gene); stripe-rust
wheat → WestBred WB1376CLP (Yellow Rust 2 = best).
- Variety-code lookups work via prefilter: DKC62-08RIB, AG29XF4,
WB6430 all return as #1 hit. BM25 confirms ranking unambiguously
(top-1 score -13.2 vs -8.5 for #2 on "DKC62-08RIB ratings").
- Server boots cleanly in stdio AND streamable-http modes.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
seed-mcp
MCP server over the public catalogs of major US row-crop seed
vendors — corn, soybeans, wheat. Sibling project to
crop-chem-docs
(pesticide labels), feeding the same Drawbar farm-advisor AI.
The server exposes per-variety records with agronomic ratings, disease tolerance, trait stack, maturity, and regional notes — so the advisor can answer questions like "which corn hybrid for sandy soil, drought-prone, RM ≤105 in northeast Iowa?" without rummaging through individual brand sites.
Vendor coverage
| Vendor | Verdict | Varieties | Notes |
|---|---|---|---|
| Bayer seeds (DEKALB + Asgrow + WestBred) | 🟢 | ~475 | Same cropscience.bayer.us Next.js infra as crop-chem-docs |
| Golden Harvest (Syngenta) | 🟢 | ~175 | Sitemap + server-rendered HTML + Syngenta CDN PDFs |
| NK (Syngenta) | 🟢 | 29 | Shares PDF fetcher with Golden Harvest |
| AgriPro (Syngenta wheat) | 🟢 | 24 | Drupal Views, server-rendered |
| Beck's PFR | 🟡 | 2,089 | Public Sanity GROQ API (no auth) |
| Beck's products | 🟡 | 860 | Identity-only until SeedIQ XHR sniffed |
| Pioneer (Corteva) | 🔴 | — | ToS bans automation — curated fallback lesson instead |
Quick start
git clone https://git.jpaul.io/justin/seed-mcp.git
cd seed-mcp
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
# Run one scraper
python -m scrape.runner --source bayer_seeds --force
# Rebuild indexes
python -m rag.index --rebuild
# Local MCP server (stdio for Claude Desktop dev)
python -m docs_mcp.server --transport stdio
Tools exposed
| Tool | Purpose |
|---|---|
search_docs |
Hybrid + rerank variety search with crop / RM / trait / region filters |
get_page |
Full variety record by (source, source_key) |
list_versions |
Discover crops, brands, traits, RM/MG ranges, wheat classes |
corpus_status |
Counts + freshness; useful for health probes |
crop_seed_api_lessons |
Curated agronomy lessons — Pioneer fallback, disease-scale normalization, regional placement heuristics |
Build phases
This is a clone of docs-mcp-template.
The 13 phases in PLAN.md apply:
| Phase | Status |
|---|---|
| 0 — scaffold | done |
| 1 — first scraper (bayer_seeds) | next |
| 2 — chunk + index | pending |
| 3 — baseline MCP tools | template defaults |
| 4-5 — Dockerfile + CI | done (placeholders filled) |
| 6 — reranker | shares llama-rerank sidecar with crop-chem-docs |
| 7 — eval harness | pending (curate ~25 queries) |
| 8 — hybrid search | done (template) |
| 9 — diff_versions, list_cluster | optional |
11 — crop_seed_api_lessons curated layer |
pending |
See CLAUDE.md for the canonical sidecar schema and the
disease-scale-normalization gotcha (Golden Harvest is reversed).
Infrastructure
- Registry:
git.jpaul.io/justin/seed-mcp:latest(Watchtower) /:corpus-YYYY.MM.DD(production pin) - Embedder: shared Ollama pool with crop-chem-docs (Gitea-host GPUs + Windows Ollama; CI never hits trashpanda's production Ollama)
- Reranker: shared
llama-reranksidecar on trashpanda's Tesla P4 (one container, both MCPs use it) - PRODUCT_NAME:
crop_seed(notseed_mcp— used in Chroma collection, BM25 db filename, andcrop_seed_api_lessonstool)