Drop the PluginToolLoader.get_tool() override that returned a raw
ComponentManifest, so every loader's get_tool() now returns a uniform
resource_tool.LLMTool (PluginToolLoader.get_tools() already did this
conversion). This removes the only source of tool-shape heterogeneity.
- ToolManager.get_tool_schema(): drop the ComponentManifest-vs-LLMTool branch
- ToolManager.get_tool_detail(): new host-level shape {name, description,
human_desc, parameters}
- handler.py GET_TOOL_DETAIL: call tool_mgr.get_tool_detail(); delete the
handler-local _build_tool_detail + _i18n_to_dict/_i18n_to_text adapters and
the litellm TODO
- ToolLookupResult is now just LLMTool
The dropped label/spec fields were not consumed by any runner (local-agent
build_llm_tool and external harnesses use only name/description/parameters).
Extract the AgentRunner Protocol v1 host-side surface from the giant
RuntimeConnectionHandler.__init__ into sibling modules using a registration-
function pattern (behavior-preserving; @h.action == @self.action):
- agent_run_support.py: shared constants + authorization/scope/projection helpers
- agent_pull_actions.py: register(h) for history/event pull APIs
- agent_runner_actions.py: register(h) for run/runtime/stats/claim lifecycle
- agent_state_actions.py: register(h) for steering/state APIs
__init__ now calls the three register(self) functions. handler.py keeps the
pre-existing plugin/llm/vector/knowledge handlers, get_prompt/call_tool/
get_tool_detail (coupled to retained helpers), shared helpers, and outbound
methods; it re-imports _validate_agent_run_session so external imports keep
working. handler.py: 4066 -> 1871 lines.
test_state_api_auth.py: repoint get_session_registry patch targets to
agent_run_support (the lookup moved modules). 385 agent unit tests pass; ruff clean.
Expose skill tools (activate/register_skill/native exec) like native tools
instead of gating them behind the skill_authoring capability:
- toolmgr.get_all_tools drops include_skill_authoring; SkillToolLoader
self-gates on sandbox + skill_mgr
- preproc drops the include_skill_authoring branch; pipeline-bound skills
and the skills resource gate on skill_mgr presence
Persist activated skills into host.activated_skills conversation state so
they survive across runs (host writes at activate; last-write-wins); drop
the dead restore_activated_skills helper.
Prefill ToolResource.parameters host-side (tool_mgr.get_tool_schema) so
runners build LLM tools without per-tool get_tool_detail round-trips.
Align agent-runner-pluginization design docs to the all-tool model.
huanghuoguoguo