claude-toolbox 0.5.1

Channel-agnostic tool library for Claude agents: fs, search, intro, git, github, web, notebook, code, shell, edit, extended modules + a unified Tool/ToolRegistry abstraction. Pip installs as `claude-toolbox`, imports as `toolbox`.

toolbox

Utility tool functions for Claude-style agents. A library of well-tested, channel-agnostic tools that any agent (Slack bot, CLI, web UI, MCP server) can pick up and hand to the LLM.

Modeled on solveit's tool organization, restructured for use across multiple agent projects.

Install

pip install claude-toolbox

The PyPI dist name is claude-toolbox (the unprefixed toolbox was taken). The Python import name stays toolbox:

import toolbox
from toolbox import fs, search, intro
from toolbox.registry import ToolRegistry

System binaries used by some tools

A few modules shell out to external binaries — install via your platform package manager (or conda install -c conda-forge ripgrep ast-grep git):

Module / tool	Needs	Install (macOS)	Install (Linux)
search.grep, search.rg	rg (ripgrep)	brew install ripgrep	apt install ripgrep
search.ast_grep	ast-grep	brew install ast-grep	apt install ast-grep
git.*	git	(preinstalled)	apt install git

Tools fail at call-time with a clear error if the binary isn't on PATH.

Two ways to use it

Path 1 — raw functions (simplest)

Just import the function and pass it where the LLM expects callables (e.g. claudette's Chat(tools=[...])):

from toolbox import fs, search, intro

chat = Chat(tools=[fs.read, fs.view, search.grep, intro.symsrc])

No abstraction, no registry. The functions have docstrings and type hints; claudette / toolslm derive schemas via get_schema.

Path 2 — explicit registry (for MCP unification, custom dispatch, multi-source)

When you need to merge local Python tools with MCP-server tools, or apply path-scoping wrappers, or curate per-agent subsets:

from toolbox import registry, fs, search, intro

r = registry.ToolRegistry()
r.add_module(fs, only=["read", "view", "ls"])     # selective subset
r.add_module(intro)                                # whole module
r.add(search.grep)                                 # one function
r.add_mcp(schema, executor, source_id="xray")     # MCP-sourced tool

# Render for Anthropic's Messages API
schemas = r.to_anthropic_tools()

# Dispatch a tool_use from a model response
result = await r.dispatch(name, **arguments)

Tools registered via add_module get module-qualified names: fs.read, search.grep, intro.symsrc. No collisions, self-documenting in tool_use blocks.

Convenience: collect_all

Build a registry pre-populated with every toolbox module:

from toolbox import collect_all

r = collect_all()                                       # all 96 tools
r = collect_all(only_modules=["fs", "search", "intro"]) # subset

What's inside

96 tools across 11 modules:

Module	Count	What's in it
fs	11	File system: read, write, view, ls, mkdir, rm, mv, cp, exists, is_file, is_dir
edit	5	Surgical file edits: str_replace, strs_replace, insert_line, replace_lines, del_lines
search	6	Code search: rg, sed, grep, ast_grep, glob_files, find
code	4	Python execution: pyrun (persistent IPython), pyrun_subprocess, eval_python, reset_shell
intro	11	Symbol introspection (re-exports from toolslm.inspecttools): symsrc, symsearch, symdir, symval, symtype, symslice, symnth, symlen, symfiles_folder, symfiles_package, importmodule
git	14	Git ops: git_status, git_diff, git_log, git_show, git_add, git_commit, git_branch, git_checkout, git_push, git_pull, git_clone, git_remote, git_stash, git_reset
github	9	GitHub API: gh_repo, gh_pr, gh_issue, gh_search_code, gh_list_files, gh_read_file, plus repo2ctx, folder2ctx, parse_gh_url from toolslm
web	9	Web fetching: read_md, read_html, read_docs, html2md, find_docs, fetch, fetch_json, fetch_bytes, web_search (DuckDuckGo)
notebook	12	Jupyter .ipynb operations: view_nb, view_cell, cell_* editors, nb_add_cell, nb_delete_cell, mk_cell, read_nb, write_nb
shell	3	Shell commands: shell, run_cmd, which
extended	12	Testing/validation: run_tests, run_pytest, run_unittest, lint_code, type_check, format_code, security_check, generate_docs, doc_preview, count_lines, dependency_graph, code_stats

Design principles

• No global state. import toolbox has no side effects — no auto-registration, no print spam, no eager imports of every dep.
• Modules over abstractions. Each module is a flat namespace of functions you can use directly. The Tool / ToolRegistry abstraction is opt-in.
• Module-qualified names in registries (fs.read, not fsread). Self-documenting and collision-free.
• Sync functions, async dispatch. Tools are sync Python functions; Tool.execute runs them off-thread when called from async code.
• Tested individually. Each tool is verified in isolation; the registry just composes them.

API reference (Path 2 essentials)

Tool

@dataclass
class Tool:
    name: str
    description: str
    schema: ToolSchema
    source: ToolSource          # INTERNAL | MCP
    executor: Callable[..., Any]
    source_id: Optional[str]
    metadata: Dict[str, Any]

    def to_anthropic_format() -> dict
    async def execute(**kwargs) -> Any

ToolRegistry

class ToolRegistry:
    def add_tool(tool: Tool) -> Tool
    def add(func, name=None, description=None) -> Tool
    def add_module(module, only=None, skip=None) -> List[Tool]
    def add_mcp(schema_dict, executor, source_id) -> Tool

    def get(name: str) -> Optional[Tool]
    def list_tools(source: Optional[ToolSource] = None) -> List[Tool]
    def list_by_source(source_id: str) -> List[Tool]

    def to_anthropic_tools() -> List[dict]            # for Anthropic API
    async def dispatch(name: str, **arguments) -> Any  # for tool_use execution

make_tool_from_function

def make_tool_from_function(
    func, name=None, description=None,
    source=ToolSource.INTERNAL, source_id=None, metadata=None,
) -> Tool

collect_all

def collect_all(only_modules: Optional[List[str]] = None) -> ToolRegistry

License

Internal library — pick a license before publishing publicly.