seed-mcp

justin/seed-mcp

Fork 0

Commit Graph

Author	SHA1	Message	Date
justin	4009dc0b78	Phase 11: crop_seed_api_lessons tool + Pioneer fallback Add the fifth MCP tool — crop_seed_api_lessons(topic?) — backed by docs_mcp/lessons.md, the ONLY source of opinionated content in the server. Everything else (search_docs, get_page, lookup_variety) returns verbatim from vendor catalogs; lessons.md fills the gaps the corpus can't cover. The Pioneer fallback is the critical anti-hallucination piece: Pioneer's ToS bans automation, so the corpus has no Pioneer data. Without this tool, an agent might surface Bayer/Asgrow chunks as mediocre matches for a Pioneer query. The tool's docstring tells the agent to call it on any Pioneer / P-series question; the 'pioneer' section says clearly: "I don't have Pioneer's variety data indexed... please consult Pioneer or an extension service." "Do NOT invent Pioneer hybrid ratings." Other lesson sections cover knowledge the agent needs to interpret search_docs / get_page output correctly: - rating-scales: Bayer 1-9, Golden Harvest 9-to-1, what R/MR/S/Rps1c/R3 mean in soybean disease columns - maturity-semantics: corn RM days vs soybean MG vs wheat class + qualitative early/medium/late - trait-glossary: SSRIB, VT2PRIB, XF, E3, Conkesta, Clearfield, etc. - scn-resistance: race coverage + Peking vs PI 88788 source - regional-listings: how to interpret Bayer's "local profiles" - sources-not-yet-indexed: which vendors aren't in the corpus yet - checking-your-work: always call lookup_variety before quoting Lesson lookup prefers slug-match (returns just `rating-scales` for topic="rating", not every section that mentions ratings); falls back to body-match only when no slug matches. Smoke-tested with topic=pioneer, topic=rating, topic=trait, topic=zzzzzz (no match), and topic=None (full index = 10K chars, 8 sections). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-25 13:18:57 -04:00
justin	a766756a05	Phase 2/3: chunker + indexer + MCP server tools 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>	2026-05-25 13:14:16 -04:00
justin	ac40e05734	seed-mcp scaffold: clone docs-mcp-template, customize for crop_seed PRODUCT_NAME Image rebuild (skip scrape) / build (push) Failing after 7s Details Sibling project to crop-chem-docs, same MCP-template lineage. Corpus is seed/hybrid varieties across 6 vendors instead of pesticide labels. What's customized vs. the template: - CLAUDE.md: vendor matrix, build priority, Pioneer fallback policy, canonical sidecar schema (per-crop), Golden Harvest disease-scale reversal gotcha, no-IPv6 / HTTPS-clone note - README.md: vendor coverage table, tool list, phase status - Dockerfile: PRODUCT_NAME=crop_seed default, sources.json (not bundles.json), HYBRID_SEARCH=true, OLLAMA_URL + RERANK_URL Docker DNS defaults (same llama-rerank sidecar as crop-chem-docs) - .gitea/workflows/refresh.yml: monthly cron (seed catalogs move slowly), 5 GREEN scraper steps, corpus-YYYY.MM.DD tag for Drawbar pinning, continue-on-error on GC step - .gitea/workflows/image-only.yml: paths filter + cancel-in-progress concurrency group - scripts/registry_gc.py: lifted from crop-chem-docs (correct Gitea packages API URL + UA header to bypass CF block on default Python-urllib UA) - sources.json: catalog of 6 sources + scope_filter + per-source schema notes + Pioneer-exclusion rationale - scrape/runner.py: dispatcher with --all = GREEN-only - scrape/sources/{bayer_seeds,golden_harvest,nk,agripro,becks_pfr, becks_products}.py: stub modules with implementation notes - docs_mcp/server.py: PRODUCT_NAME default → crop_seed, PRODUCT_DOCS_URL → repo URL Pioneer is intentionally NOT a source. ToS bans automation; dealer locator is login-gated. The MCP returns a curated fallback lesson directing the user to pioneer.com. Next phases: - Phase 1: implement bayer_seeds (lift-and-shift from crop-chem-docs Bayer scraper; same __NEXT_DATA__ infra) - Phase 7: curate eval/queries.jsonl - Phase 11: lessons.md with Pioneer fallback + disease-scale notes Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-25 12:28:49 -04:00

Author

SHA1

Message

Date

justin

4009dc0b78

Phase 11: crop_seed_api_lessons tool + Pioneer fallback

Add the fifth MCP tool — crop_seed_api_lessons(topic?) — backed by
docs_mcp/lessons.md, the ONLY source of opinionated content in the
server. Everything else (search_docs, get_page, lookup_variety)
returns verbatim from vendor catalogs; lessons.md fills the gaps
the corpus can't cover.

The Pioneer fallback is the critical anti-hallucination piece:
Pioneer's ToS bans automation, so the corpus has no Pioneer data.
Without this tool, an agent might surface Bayer/Asgrow chunks as
mediocre matches for a Pioneer query. The tool's docstring tells
the agent to call it on any Pioneer / P-series question; the
'pioneer' section says clearly:

  "I don't have Pioneer's variety data indexed... please consult
  Pioneer or an extension service."

  "Do NOT invent Pioneer hybrid ratings."

Other lesson sections cover knowledge the agent needs to interpret
search_docs / get_page output correctly:

- rating-scales: Bayer 1-9, Golden Harvest 9-to-1, what
  R/MR/S/Rps1c/R3 mean in soybean disease columns
- maturity-semantics: corn RM days vs soybean MG vs wheat class +
  qualitative early/medium/late
- trait-glossary: SSRIB, VT2PRIB, XF, E3, Conkesta, Clearfield, etc.
- scn-resistance: race coverage + Peking vs PI 88788 source
- regional-listings: how to interpret Bayer's "local profiles"
- sources-not-yet-indexed: which vendors aren't in the corpus yet
- checking-your-work: always call lookup_variety before quoting

Lesson lookup prefers slug-match (returns just `rating-scales` for
topic="rating", not every section that mentions ratings); falls
back to body-match only when no slug matches.

Smoke-tested with topic=pioneer, topic=rating, topic=trait,
topic=zzzzzz (no match), and topic=None (full index = 10K chars,
8 sections).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-25 13:18:57 -04:00

justin

a766756a05

Phase 2/3: chunker + indexer + MCP server tools

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>

2026-05-25 13:14:16 -04:00

justin

ac40e05734

seed-mcp scaffold: clone docs-mcp-template, customize for crop_seed PRODUCT_NAME

Image rebuild (skip scrape) / build (push) Failing after 7s

Details

Sibling project to crop-chem-docs, same MCP-template lineage. Corpus is
seed/hybrid varieties across 6 vendors instead of pesticide labels.

What's customized vs. the template:
- CLAUDE.md: vendor matrix, build priority, Pioneer fallback policy,
  canonical sidecar schema (per-crop), Golden Harvest disease-scale
  reversal gotcha, no-IPv6 / HTTPS-clone note
- README.md: vendor coverage table, tool list, phase status
- Dockerfile: PRODUCT_NAME=crop_seed default, sources.json (not
  bundles.json), HYBRID_SEARCH=true, OLLAMA_URL + RERANK_URL Docker
  DNS defaults (same llama-rerank sidecar as crop-chem-docs)
- .gitea/workflows/refresh.yml: monthly cron (seed catalogs move
  slowly), 5 GREEN scraper steps, corpus-YYYY.MM.DD tag for Drawbar
  pinning, continue-on-error on GC step
- .gitea/workflows/image-only.yml: paths filter + cancel-in-progress
  concurrency group
- scripts/registry_gc.py: lifted from crop-chem-docs (correct Gitea
  packages API URL + UA header to bypass CF block on default
  Python-urllib UA)
- sources.json: catalog of 6 sources + scope_filter + per-source
  schema notes + Pioneer-exclusion rationale
- scrape/runner.py: dispatcher with --all = GREEN-only
- scrape/sources/{bayer_seeds,golden_harvest,nk,agripro,becks_pfr,
  becks_products}.py: stub modules with implementation notes
- docs_mcp/server.py: PRODUCT_NAME default → crop_seed,
  PRODUCT_DOCS_URL → repo URL

Pioneer is intentionally NOT a source. ToS bans automation; dealer
locator is login-gated. The MCP returns a curated fallback lesson
directing the user to pioneer.com.

Next phases:
- Phase 1: implement bayer_seeds (lift-and-shift from crop-chem-docs
  Bayer scraper; same __NEXT_DATA__ infra)
- Phase 7: curate eval/queries.jsonl
- Phase 11: lessons.md with Pioneer fallback + disease-scale notes

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-25 12:28:49 -04:00

3 Commits