toolery 0.0.3

A searchable, self-maintaining catalog over any corpus of tools, skills, agents, and components.

toolery

A searchable, self-maintaining catalog over any corpus of tools, skills, agents, and components.

Point toolery at a collection of heterogeneous assets — Claude skills, agent specs, MCP tools, docs, or packages — and get one searchable catalog: ask "what do I already have for X?" and get a ranked answer, not fifty schemas.

pip install toolery

Quick start

Search a folder of notes/docs from the command line:

toolery search "dedupe a csv" ~/my/notes

Or from Python — the simplest thing that works, with zero configuration:

import toolery

cat = toolery.catalog("~/my/notes")        # harvest a folder of markdown
for card, score in cat.search("parse pdf"):
    print(score, card.name, card.source_uri)

Out of the box the search is a fast, dependency-free lexical scorer, so nothing to install, no models, no API keys.

Any corpus, any asset kind

A catalog is built from sources. A source is a folder, a built-in harvester, or bare cards:

import toolery

cat = toolery.catalog(
    toolery.skills("~/.claude/skills"),     # Claude Agent Skills (SKILL.md)
    "~/my/notes",                           # a folder of docs
    [toolery.Card("grep", "tool", "grep", "search text with patterns")],
)
cat.search("find text in files")
cat.by_kind("skill")
cat.kinds                                   # {'skill': 42, 'doc': 118, 'tool': 1}

Everything is projected onto one uniform record, the Card (id, kind, name, description, tags, source_uri, content_ref). Supporting a new asset kind (agent specs, MCP tool schemas, packages) is just another generator that yields Cards — nothing else changes.

Bring your own search

catalog(...) and Catalog(...) accept a search_backend — any callable (query, cards, *, limit) -> [(card, score), ...]. The default, toolery.lexical_search, needs no dependencies. A semantic backend built on the ir retrieval substrate drops into the same seam (planned), so you can start lexical and upgrade to embeddings without changing your code.

from toolery import Catalog, lexical_search

cat = Catalog(cards, search_backend=lexical_search)   # or your own backend

Status

Early (0.x). The lexical core, the Card/harvester/catalog model, and the CLI are in place; an ir-backed semantic backend and more built-in harvesters (agents, MCP tools, packages) are next.