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Dog-specific aging tools: HAGR dog rows, dog orthologs, dose translation, DAP codebooks, FDA FOI.
About
Dog-specific aging tools: HAGR dog rows, dog orthologs, dose translation, DAP codebooks, FDA FOI.
Security Report
Valid MCP server (1 strong, 3 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
9 files analyzed · 1 issue found
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What You'll Need
Set these up before or after installing:
Environment variable: DOG_GERO_DATA
Environment variable: DOG_GERO_DB_URL
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-w0lph-dog-geroscience-mcp": {
"env": {
"DOG_GERO_DATA": "your-dog-gero-data-here",
"DOG_GERO_DB_URL": "your-dog-gero-db-url-here"
},
"args": [
"dog-geroscience-mcp"
],
"command": "uvx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
k9 — AI tooling for canine longevity research
Research brief: canine-longevity-ai-opportunities.md (landscape, ranked opportunities, and the Phase 1 plan in Section 7).
Phase 1 components:
| Directory | What | Status |
|---|---|---|
| corpus/ | canine-aging-corpus: reproducible Europe PMC corpus of the canine aging literature (records, JATS→Markdown full text from Europe PMC or NCBI, manifest, version file). | Complete: 3,565 records (2,185 core); full text for 1,140 of 1,347 PMCIDs (the other 207 are publisher-restricted from XML distribution). |
| mcp/ | dog-geroscience-mcp: MCP server with 17 dog-specific aging tools (AnAge/DrugAge/GenAge dog rows, dog orthologs via Ensembl with caching, FDA Km dose translation, Dog Aging Project codebooks across 9 releases, NIH RePORTER, BM25 corpus search with full-text retrieval, FOI summaries and structured records, intervention dossier). | Complete: 23 offline tests, smoke-tested against live Ensembl/RePORTER and over stdio; DB built from the finished corpus and FOI layers. |
| questions/ | canine-geroscience-questions: 133 questions across 10 categories, each with a gold answer and a verbatim quote from a corpus record, enforced by a validator; plus a BM25 retrieval baseline. | v0.1: drafted, validator-clean, then reviewed item-by-item by an independent LLM pass (9 fixes, 1 drop); recall@5 = 0.985. No domain-expert review yet. |
Phase 2 components:
| Directory | What | Status |
|---|---|---|
| foi/ | foi-summaries: FDA CVM Freedom of Information summaries as a public-domain dataset (index of 1,726 summaries, 497 dog-product PDFs, text, parsed sections and General Information fields, species flags) plus a typed, quote-validated extraction of all 497 summaries (1,026 PK values, 193 safety studies, 414 effectiveness studies, 918 adverse-reaction rows). | Complete: 497 records, 97% with parsed sections; structured layer covers all 497 (4,510 quotes, 0 errors, 79 explained warnings). |
mcp/ (extended) | intervention_dossier (synonym-aware, species-filtered), foi_summary_search, foi_summary_get, foi_structured_search, the dossier_briefing prompt; scripts/dossier_eval.py coverage table over ITP compounds. | Complete; 23 tests. |
| docs/ | Static evidence site generated from the database by mcp/scripts/build_site.py: one page per NIA ITP compound and veterinary comparator (DrugAge rows, dog-equivalent doses, canine literature, FDA FOI summaries, gaps) and one per FOI ingredient (dose regimen, PK, target-animal safety, effectiveness, adverse reactions, each with its verbatim quote), plus llms.txt and a sitemap. No model-written text. | Live at https://w0lph.github.io/k9/ (219 pages); rebuild with cd mcp && uv run python scripts/build_site.py. |
.\rebuild.ps1 regenerates everything in dependency order (-Fresh re-fetches sources).
PUBLISHING.md is the step-by-step for the Hugging Face Hub (dataset cards and
staging script in publish/), PyPI and the MCP registry (mcp/server.json), and Glama
(glama.json, root Dockerfile). The server installs with
uvx dog-geroscience-mcp (PyPI), as a Claude Desktop extension (.mcpb on the releases page),
or as a Claude Code plugin with a routing skill (/plugin marketplace add w0lph/k9, then
/plugin install dog-geroscience@k9; sources in plugins/), and downloads its database on
first run. Each directory is its own
uv project:
cd corpus && uv sync --extra dev && uv run pytest -q
cd mcp && uv sync && uv run dog-geroscience-mcp build && uv run pytest -q
cd questions && uv sync && uv run pytest -q && uv run cgq validate data/canine_geroscience_v0.jsonl --require-ids
cd foi && uv sync && uv run foi run && uv run pytest -q
cd mcp && uv run dog-geroscience-mcp build --skip-download # picks up ../foi/data/foi_summaries_dog.jsonl
Large derived data (corpus/data, mcp/data, FOI PDFs and text) is git-ignored; it is
rebuilt by the pipelines and published as Hugging Face datasets (publish/).
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