refactor(core): 工具注册声明式化 core/tool_registry.py——四种 env-gate 风格收敛为一张表

架构审查 P0#3:build_agent 内 ~220 行注册代码,同一个「有 key/开关才注册」
意图混用四种写法(inline getenv / *_configured() / Config.load() 返 None /
caps 开关),范式靠注释口头传承。

- 新增 core/tool_registry.py:注册表 = (组名, gate, factory) 列表。gate 统一
  零参 bool(§7.5 #7 "secret-bearing 工具仅 env 存在才注册"由此获得代码强制);
  factory 内做 variant 选择(seedance/vision 取 yaml 段),选不出返 [] 自然不挂;
  新增工具 = 表里加一行
- ToolContext 承载 build_agent 已解析的上下文(路径/身份/媒体配置),注册层
  不重复读 env/yaml
- agent_builder.py 892→649 行:注册块换成一次 build_tools 调用,26 个工具类
  import 随之迁走

验证:292 测试全过;真实配置冒烟 32 个工具逐名符合预期,scheduled_run
门控(少 4 个 schedule_*)生效。

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
caoqianming 2026-07-23 10:36:38 +08:00
parent 7354578aaa
commit 1197d73432
2 changed files with 251 additions and 252 deletions

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@ -40,37 +40,8 @@ from core.sinks import ConsoleEventSink
from core.skills import SkillRegistry
from core.storage import check_no_subtask
from core.task import TaskState
from tools.fs import EditTool, GlobTool, GrepTool, ReadTool, WriteTool
from tools.documents import DocumentDownloadTool, DocumentListKbTool, DocumentSearchTool
from tools.materials_project import (
MaterialsProjectGetEntriesTool,
MaterialsProjectGetStructureTool,
MaterialsProjectSearchSummaryTool,
)
from tools.look_at_image import LookAtImageTool
from tools.read_document import ReadDocumentTool
from tools.check_process import CheckProcessTool
from tools.run_python import RunPythonTool
from tools.seedance import SeedanceTool
from tools.gpt_image import GptImageTool
from tools.seedream import SeedreamTool
from tools.shell import ShellTool
from tools.skill_authoring import ForkSkillTool, SaveSkillTool
from tools.skill_tool import LoadSkillTool
from tools.task_progress import TaskProgressTool
from tools.ask_user import AskUserTool
from tools.web_fetch import WebFetchTool
from tools.web_search import WebSearchTool
from tools.schedule import (
ScheduleCancelTool, ScheduleCreateTool, ScheduleListTool, ScheduleUpdateTool,
)
from tools.send_email import SendEmailTool, smtp_configured
from tools.transcribe_audio import TranscribeAudioTool
from tools.wechat_bot import WechatPushTool, wechat_push_available
from core.ark_client import ArkConfig
from core.asr_lfasr import is_configured as lfasr_configured
from core.bocha_client import BochaConfig
from core.tool_registry import ToolContext, build_tools
# 媒体工具指引:仅当本 run 真的挂了对应媒体工具才追加进 system prompt —— 没 key 的
@ -592,228 +563,14 @@ def build_agent(
# 同时让 web SPA artifact chip 抽取稳定锚定 <wd>/ 前缀)
ur_path = user_root(workspace_dir, uid)
tools = {}
tp = TaskProgressTool(base_dir=tool_base, user_root=ur_path)
tools[tp.name] = tp
au = AskUserTool(base_dir=tool_base, user_root=ur_path)
tools[au.name] = au
for cls in (ReadTool, WriteTool, EditTool, GlobTool, GrepTool):
t = cls(base_dir=tool_base, user_root=ur_path)
tools[t.name] = t
# shell/run_python 带 task_id:background=true 的 bg proc 状态锚定
# `<user_root>/.zcbot_procs/<task_id>/`(DESIGN §8.12)。check_process 是它们的
# 配套查询/终止工具,host in-process(docker 模式下状态文件也在宿主侧)。
sh = ShellTool(base_dir=tool_base, user_root=ur_path, task_id=task_id)
tools[sh.name] = sh
cp = CheckProcessTool(task_id=task_id, base_dir=tool_base, user_root=ur_path)
tools[cp.name] = cp
# web_fetch 无需 API key,始终可用
wf = WebFetchTool(base_dir=tool_base, user_root=ur_path)
tools[wf.name] = wf
# Secret-bearing domain tools stay host-side. Never expose DOCUMENT_SEARCH_API_KEY
# / MP_API_KEY to run_python or the sandbox; only register typed tools when the
# corresponding host env exists.
if os.getenv("DOCUMENT_SEARCH_API_KEY", "").strip():
for t in (
DocumentListKbTool(base_dir=tool_base, user_root=ur_path),
DocumentSearchTool(base_dir=tool_base, user_root=ur_path),
DocumentDownloadTool(
working_dir=working_dir_path,
base_dir=tool_base,
user_root=ur_path,
),
):
tools[t.name] = t
if os.getenv("MP_API_KEY", "").strip():
for t in (
MaterialsProjectSearchSummaryTool(
working_dir=working_dir_path,
base_dir=tool_base,
user_root=ur_path,
),
MaterialsProjectGetStructureTool(
working_dir=working_dir_path,
base_dir=tool_base,
user_root=ur_path,
),
MaterialsProjectGetEntriesTool(
working_dir=working_dir_path,
base_dir=tool_base,
user_root=ur_path,
),
):
tools[t.name] = t
if skills.skills:
# LoadSkillTool 返回头里的 dir 由 registry 按 skill.source 给容器内路径
# (内置 → /sandbox/skills,用户 → /workspace/.skills);host backend → host 绝对路径。
ls = LoadSkillTool(registry=skills, base_dir=tool_base, user_root=ur_path)
tools[ls.name] = ls
# 用户 skill 创作工具:恒挂(每个用户都能造自己的 skill)。host-side 直接写
# user_root/.skills —— 不走沙箱 fs(其 base_dir 锚 cwd / 容器 wd,够不到 .skills)。
user_skills_dir = ur_path / ".skills"
for t in (
SaveSkillTool(user_skills_dir, skills, base_dir=tool_base, user_root=ur_path),
ForkSkillTool(user_skills_dir, skills, base_dir=tool_base, user_root=ur_path),
):
tools[t.name] = t
# 定时任务管理(DESIGN §8.5):增删查三件套。**定时 run 内不挂**(防任务造任务,
# 自我繁殖);仅交互对话里能建/管 job。user_id 由 ctor 注入,不信模型传的 id。
if not scheduled_run:
for t in (
ScheduleCreateTool(uid, base_dir=tool_base, user_root=ur_path),
ScheduleListTool(uid, base_dir=tool_base, user_root=ur_path),
ScheduleUpdateTool(uid, base_dir=tool_base, user_root=ur_path),
ScheduleCancelTool(uid, base_dir=tool_base, user_root=ur_path),
):
tools[t.name] = t
# 发邮件(§8.5 投递):仅当 SMTP_* env 齐了才挂(沿用"有 key 才注册",没配的
# 部署里 agent 看不到一个永远报错的工具)。定时与交互 run 都可用。
# base_dir 用 working_dir_path(该 task 的**宿主**工作目录绝对路径),不是 tool_base(cwd)。
# send_email 在宿主进程读附件文件,docker 下 agent 给的相对路径相对容器 workdir=task_dir,
# 翻回宿主即 working_dir_path;tool 内 _resolve_user_file 再处理 /workspace 容器绝对路径。
if smtp_configured():
se = SendEmailTool(base_dir=working_dir_path, user_root=ur_path)
tools[se.name] = se
# 微信主动推送(§8.7 渠道抽象):仅当微信渠道开关在才挂(沿用"有开关才注册")。
# 交互与定时 run 都可用(定时简报可主动推回用户微信,24h 窗口内)。user_id ctor 注入。
# base_dir 同 send_email:用 working_dir_path(宿主 task 目录),wechat_push 在宿主进程
# 读待发文件,需把 agent 给的相对/容器路径翻回宿主(详 _resolve_user_file)。
if wechat_push_available():
wp = WechatPushTool(uid, base_dir=working_dir_path, user_root=ur_path, task_id=task_id)
tools[wp.name] = wp
if caps.enable_run_python:
rp = RunPythonTool(base_dir=tool_base, user_root=ur_path, task_id=task_id)
tools[rp.name] = rp
# 每账号每日配额(yaml `quotas` 段,跨 task 跨 variant 全口径合计;
# 0 / 缺失 = 不限)。tool 起手 check_daily_quota,超额返 [Error] 不调远端。
quotas = cfg.get("quotas") or {}
images_per_day = int(quotas.get("images_per_day", 0))
videos_per_day = int(quotas.get("videos_per_day", 0))
# 图像生成 tool:跨 provider 二选一(选择已在函数上半部 _choose_image_variant 定好,
# 与 system prompt 媒体段同源)。没任何 provider key → 不挂,用户无感知。
# 本次 run 锁定该 variant,run 内多次 tool call 全用同一个;下一条消息可重选。
if img_cfg is not None:
if img_provider == "doubao":
image_tool: Any = SeedreamTool(
ark_cfg=img_provider_cfg,
image_variant_cfg=img_cfg,
variant_key=img_key,
working_dir=working_dir_path,
task_id=task_id,
user_id=uid,
base_dir=tool_base,
user_root=ur_path,
daily_limit=images_per_day,
)
else: # unifyllm
image_tool = GptImageTool(
gw_cfg=img_provider_cfg,
image_variant_cfg=img_cfg,
variant_key=img_key,
working_dir=working_dir_path,
task_id=task_id,
user_id=uid,
base_dir=tool_base,
user_root=ur_path,
daily_limit=images_per_day,
)
tools[image_tool.name] = image_tool
# 视频 / 看图 tool 仍豆包独有:仅当 ARK_API_KEY 设了才挂。
if ark_cfg is not None:
# 视频 variant 选择(同 image_variant 范式):video_variant 由 caller 传,
# 空 → 取 yaml 第一个 video variant。本 run 的 SeedanceTool 锁定该 variant。
# cancel_check 是 web 入口构造的 `lambda: broker.is_cancelled(task_id)` —— 轮询
# 期间(典型 30-90s)拿来响应用户停止按钮;远端 cgt 任务无 cancel API,best-effort 不动远端
video_cfg = (ark_cfg.raw.get("video") or {})
v_chosen_key, v_chosen_cfg = "", None
if video_variant:
v = video_cfg.get(video_variant)
if isinstance(v, dict):
v_chosen_key, v_chosen_cfg = video_variant, v
if v_chosen_cfg is None:
for variant_key, variant_cfg in video_cfg.items():
if isinstance(variant_cfg, dict):
v_chosen_key, v_chosen_cfg = variant_key, variant_cfg
break
if v_chosen_cfg is not None:
seedance_tool = SeedanceTool(
ark_cfg=ark_cfg,
video_variant_cfg=v_chosen_cfg,
variant_key=v_chosen_key,
working_dir=working_dir_path,
task_id=task_id,
user_id=uid,
base_dir=tool_base,
user_root=ur_path,
cancel_check=cancel_check,
daily_limit=videos_per_day,
)
tools[seedance_tool.name] = seedance_tool
# 图像理解 tool(look_at_image / 豆包 Seed 2.0 Lite vision):仅当 yaml 有 vision 段才挂。
# 无 variant 选择维度(读图不分档,固定第一个 variant),与 image/video 的"用户可切档"不同。
vision_cfg = (ark_cfg.raw.get("vision") or {})
vis_key, vis_variant = "", None
for variant_key, variant_cfg in vision_cfg.items():
if isinstance(variant_cfg, dict):
vis_key, vis_variant = variant_key, variant_cfg
break
if vis_variant is not None:
look_tool = LookAtImageTool(
ark_cfg=ark_cfg,
vision_variant_cfg=vis_variant,
variant_key=vis_key,
working_dir=working_dir_path,
task_id=task_id,
user_id=uid,
base_dir=tool_base,
user_root=ur_path,
)
tools[look_tool.name] = look_tool
# 文档理解(read_document):同 variant 同 key,扫描件 PDF OCR(markitdown 死路补位)
readdoc_tool = ReadDocumentTool(
ark_cfg=ark_cfg,
vision_variant_cfg=vis_variant,
variant_key=vis_key,
working_dir=working_dir_path,
task_id=task_id,
user_id=uid,
base_dir=tool_base,
user_root=ur_path,
)
tools[readdoc_tool.name] = readdoc_tool
# 录音文件转写(transcribe_audio / 讯飞 LFASR):仅当 XFYUN_APPID +
# XFYUN_LFASR_SECRET_KEY 齐了才挂(沿用"有 key 才注册")。与 IAT 语音听写是两个
# 服务、两套 key。cancel_check 同 seedance:轮询期(短则十几秒长则几分钟)响应停止按钮。
if lfasr_configured():
ta = TranscribeAudioTool(
working_dir=working_dir_path,
base_dir=tool_base,
user_root=ur_path,
cancel_check=cancel_check,
)
tools[ta.name] = ta
# 博查联网搜索:仅当 BOCHA_API_KEY 设了才挂
bocha_cfg = BochaConfig.load()
if bocha_cfg is not None:
ws = WebSearchTool(cfg=bocha_cfg)
tools[ws.name] = ws
tools = build_tools(ToolContext(
tool_base=tool_base, ur_path=ur_path, working_dir_path=working_dir_path,
task_id=task_id, uid=uid, cfg=cfg, caps=caps, skills=skills,
cancel_check=cancel_check, scheduled_run=scheduled_run,
ark_cfg=ark_cfg, img_provider=img_provider, img_key=img_key,
img_cfg=img_cfg, img_provider_cfg=img_provider_cfg,
video_variant=video_variant,
))
sink = ConsoleEventSink(console) if console else None
# §7.5 #5/#6 Executor 抽象:env `ZCBOT_SANDBOX_BACKEND=host|docker` 切 backend。

242
core/tool_registry.py Normal file
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@ -0,0 +1,242 @@
"""声明式工具注册表(§3.4 / §7.5 #7;从 agent_builder.build_agent 析出,2026-07-23)。
背景:此前 build_agent ~220 行工具注册,同一个 key/开关才注册意图用了
四种写法(inline getenv / `*_configured()` 助手 / `Config.load()` None / caps
开关),约定靠注释里的"沿用范式"口头传承本模块把它收敛为一张表:
(组名, gate, factory)
- gate:零参 -> boolenv key / 渠道开关 / 能力开关全走这一种形态,False 则该组
工具对本 run 不存在(agent 看不到一个永远报错的工具 §7.5 #7 红线的代码强制)。
- factory:零参 -> list[Tool]变体选择( seedance/vision yaml 第一个 variant)
factory 里做,选不出返 [] 即自然不挂
新增工具 = 表里加一行ToolContext build_agent 已解析好的上下文(路径 / 身份 /
媒体配置),factory ctx 取参 不在本模块重复读 env / yaml
"""
from __future__ import annotations
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Callable, Optional
from uuid import UUID
from tools.ask_user import AskUserTool
from tools.check_process import CheckProcessTool
from tools.documents import DocumentDownloadTool, DocumentListKbTool, DocumentSearchTool
from tools.fs import EditTool, GlobTool, GrepTool, ReadTool, WriteTool
from tools.gpt_image import GptImageTool
from tools.look_at_image import LookAtImageTool
from tools.materials_project import (
MaterialsProjectGetEntriesTool,
MaterialsProjectGetStructureTool,
MaterialsProjectSearchSummaryTool,
)
from tools.read_document import ReadDocumentTool
from tools.run_python import RunPythonTool
from tools.schedule import (
ScheduleCancelTool, ScheduleCreateTool, ScheduleListTool, ScheduleUpdateTool,
)
from tools.seedance import SeedanceTool
from tools.seedream import SeedreamTool
from tools.send_email import SendEmailTool, smtp_configured
from tools.shell import ShellTool
from tools.skill_authoring import ForkSkillTool, SaveSkillTool
from tools.skill_tool import LoadSkillTool
from tools.task_progress import TaskProgressTool
from tools.transcribe_audio import TranscribeAudioTool
from tools.web_fetch import WebFetchTool
from tools.web_search import WebSearchTool
from tools.wechat_bot import WechatPushTool, wechat_push_available
from core.asr_lfasr import is_configured as lfasr_configured
from core.bocha_client import BochaConfig
@dataclass
class ToolContext:
"""build_agent 解析好的、工具装配需要的全部上下文。"""
tool_base: Path # fs/shell 类工具的 base_dir(cwd / task 目录)
ur_path: Path # user_root(输出渲染相对路径 + host-side 落点)
working_dir_path: Path # 该 task 的宿主工作目录绝对路径
task_id: str
uid: UUID
cfg: dict # config/agent.yaml(quotas 段)
caps: Any # ModelCapabilities(enable_run_python)
skills: Any # SkillRegistry
cancel_check: Optional[Callable[[], bool]]
scheduled_run: bool
# 媒体(上游已 load 一次,避免重复读 yaml):
ark_cfg: Any # ArkConfig | None(豆包;None=ARK_API_KEY 缺)
img_provider: str # "doubao" / "unifyllm" / ""
img_key: str # 选中的 image variant key
img_cfg: Optional[dict] # 选中的 image variant 配置(None=不挂图像工具)
img_provider_cfg: Any # 该 provider 的 ArkConfig
video_variant: str # caller 指定的 video variant key(空=yaml 第一个)
def _env_set(name: str) -> Callable[[], bool]:
return lambda: bool(os.getenv(name, "").strip())
def _pick_variant(section: dict, preferred: str = "") -> tuple[str, Optional[dict]]:
"""从 yaml 段选 variant:preferred 命中优先,否则第一个 dict 条目;无 → ("", None)。"""
if preferred:
v = section.get(preferred)
if isinstance(v, dict):
return preferred, v
for key, v in section.items():
if isinstance(v, dict):
return key, v
return "", None
def build_tools(ctx: ToolContext) -> dict[str, Any]:
"""按注册表装配本 run 的工具集。gate=False / factory 返 [] 的组自然不挂。"""
base = dict(base_dir=ctx.tool_base, user_root=ctx.ur_path)
wd_base = dict(base_dir=ctx.working_dir_path, user_root=ctx.ur_path)
quotas = ctx.cfg.get("quotas") or {}
images_per_day = int(quotas.get("images_per_day", 0))
videos_per_day = int(quotas.get("videos_per_day", 0))
def _core() -> list:
# shell/run_python 带 task_id:background=true 的 bg proc 状态锚定
# `<user_root>/.zcbot_procs/<task_id>/`(DESIGN §8.12)。check_process 是它们的
# 配套查询/终止工具,host in-process(docker 模式下状态文件也在宿主侧)。
# web_fetch 无需 API key,始终可用。
return [
TaskProgressTool(**base),
AskUserTool(**base),
ReadTool(**base), WriteTool(**base), EditTool(**base),
GlobTool(**base), GrepTool(**base),
ShellTool(task_id=ctx.task_id, **base),
CheckProcessTool(task_id=ctx.task_id, **base),
WebFetchTool(**base),
]
def _document_search() -> list:
return [
DocumentListKbTool(**base),
DocumentSearchTool(**base),
DocumentDownloadTool(working_dir=ctx.working_dir_path, **base),
]
def _materials_project() -> list:
return [
MaterialsProjectSearchSummaryTool(working_dir=ctx.working_dir_path, **base),
MaterialsProjectGetStructureTool(working_dir=ctx.working_dir_path, **base),
MaterialsProjectGetEntriesTool(working_dir=ctx.working_dir_path, **base),
]
def _load_skill() -> list:
# LoadSkillTool 返回头里的 dir 由 registry 按 skill.source 给容器内路径
# (内置 → /sandbox/skills,用户 → /workspace/.skills);host backend → host 绝对路径。
return [LoadSkillTool(registry=ctx.skills, **base)]
def _skill_authoring() -> list:
# 用户 skill 创作:恒挂(每个用户都能造自己的 skill)。host-side 直接写
# user_root/.skills —— 不走沙箱 fs(其 base_dir 锚 cwd / 容器 wd,够不到 .skills)。
d = ctx.ur_path / ".skills"
return [
SaveSkillTool(d, ctx.skills, **base),
ForkSkillTool(d, ctx.skills, **base),
]
def _schedules() -> list:
# 定时任务管理(§8.5)增删查。user_id 由 ctor 注入,不信模型传的 id。
return [
ScheduleCreateTool(ctx.uid, **base),
ScheduleListTool(ctx.uid, **base),
ScheduleUpdateTool(ctx.uid, **base),
ScheduleCancelTool(ctx.uid, **base),
]
def _send_email() -> list:
# base_dir 用 working_dir_path(宿主 task 目录):send_email 在宿主进程读附件,
# docker 下 agent 给的相对路径相对容器 workdir=task_dir,翻回宿主即 working_dir_path;
# tool 内 _resolve_user_file 再处理 /workspace 容器绝对路径。
return [SendEmailTool(**wd_base)]
def _wechat_push() -> list:
# base_dir 同 send_email(宿主进程读待发文件)。交互与定时 run 都可用
# (定时简报可主动推回用户微信,24h 窗口内)。
return [WechatPushTool(ctx.uid, task_id=ctx.task_id, **wd_base)]
def _run_python() -> list:
return [RunPythonTool(task_id=ctx.task_id, **base)]
def _image() -> list:
# 图像生成跨 provider 二选一(选择在 build_agent 上半部定好,与 system prompt
# 媒体段同源);本次 run 锁定该 variant,下一条消息可重选。
if ctx.img_cfg is None:
return []
cls_kwargs = dict(
image_variant_cfg=ctx.img_cfg, variant_key=ctx.img_key,
working_dir=ctx.working_dir_path, task_id=ctx.task_id, user_id=ctx.uid,
daily_limit=images_per_day, **base,
)
if ctx.img_provider == "doubao":
return [SeedreamTool(ark_cfg=ctx.img_provider_cfg, **cls_kwargs)]
return [GptImageTool(gw_cfg=ctx.img_provider_cfg, **cls_kwargs)]
def _video() -> list:
# 视频仍豆包独有。cancel_check:轮询期(典型 30-90s)响应用户停止按钮;
# 远端 cgt 任务无 cancel API,best-effort 不动远端。
key, v = _pick_variant(ctx.ark_cfg.raw.get("video") or {}, ctx.video_variant)
if v is None:
return []
return [SeedanceTool(
ark_cfg=ctx.ark_cfg, video_variant_cfg=v, variant_key=key,
working_dir=ctx.working_dir_path, task_id=ctx.task_id, user_id=ctx.uid,
cancel_check=ctx.cancel_check, daily_limit=videos_per_day, **base,
)]
def _vision() -> list:
# 看图 + 文档理解共用 vision variant(读图不分档,固定第一个)。
key, v = _pick_variant(ctx.ark_cfg.raw.get("vision") or {})
if v is None:
return []
kw = dict(
ark_cfg=ctx.ark_cfg, vision_variant_cfg=v, variant_key=key,
working_dir=ctx.working_dir_path, task_id=ctx.task_id, user_id=ctx.uid,
**base,
)
return [LookAtImageTool(**kw), ReadDocumentTool(**kw)]
def _transcribe() -> list:
# 录音文件转写(讯飞 LFASR,与 IAT 听写是两个服务两套 key)。
return [TranscribeAudioTool(
working_dir=ctx.working_dir_path, cancel_check=ctx.cancel_check, **base,
)]
def _web_search() -> list:
return [WebSearchTool(cfg=BochaConfig.load())]
# ── 注册表:(组名, gate, factory)。gate 判定统一零参 bool;新工具在此加行 ──
registry: list[tuple[str, Callable[[], bool], Callable[[], list]]] = [
("core", lambda: True, _core),
# Secret-bearing 域工具一律 host-side、仅对应 env 存在才注册(§7.5 #7):
# key 绝不进 run_python / 沙箱。
("document_search", _env_set("DOCUMENT_SEARCH_API_KEY"), _document_search),
("materials_project", _env_set("MP_API_KEY"), _materials_project),
("load_skill", lambda: bool(ctx.skills.skills), _load_skill),
("skill_authoring", lambda: True, _skill_authoring),
# 定时 run 内不挂 schedule_*(防任务造任务自我繁殖);仅交互对话可建/管 job。
("schedules", lambda: not ctx.scheduled_run, _schedules),
("send_email", smtp_configured, _send_email),
("wechat_push", wechat_push_available, _wechat_push),
("run_python", lambda: ctx.caps.enable_run_python, _run_python),
("image", lambda: ctx.img_cfg is not None, _image),
("video", lambda: ctx.ark_cfg is not None, _video),
("vision", lambda: ctx.ark_cfg is not None, _vision),
("transcribe_audio", lfasr_configured, _transcribe),
("web_search", lambda: BochaConfig.load() is not None, _web_search),
]
tools: dict[str, Any] = {}
for _name, gate, factory in registry:
if gate():
for t in factory():
tools[t.name] = t
return tools