docs: describe a hosted search on the Anthropic surface

The ``messages_bridge`` bullet says a server tool is the surface's own business
(only ``tool_use`` becomes a local call), the provider-tools bullet says the
Messages surface declares its own definition, the ``anthropic/model.py`` bullet
lists the blocks that now decode, and the file map says as much.

AGENTS.md

@@ -50,7 +50,7 @@ lmproto/deepseek/anthropic/     ← DeepSeek Anthropic client (extends Anthropic
 - **`openai_compatible/config.py`** — `OpenAIConfig` (token + base URL).
 - **`openai/model.py`** — OpenAI Responses API (`ResponsesCreateParam`, `Response`, function_call items, stream events).
 - **`openai/client.py`** — platform `OpenAIClient` (`kind="responses"`): chat completions + `responses` / `get_response` / `delete_response`.
-- **`anthropic/model.py`** — Anthropic Messages request/response + SSE event models.
+- **`anthropic/model.py`** — Anthropic Messages request/response + SSE event models; the response content union also carries the server-tool blocks DeepSeek's compat returns (`thinking`, `server_tool_use`, `web_search_tool_result` with its verbatim `encrypted_content`), and `tools` accepts opaque server-tool dicts.
 - **`anthropic/client.py`** — `AnthropicClient` (`kind="messages"`): `POST /messages` + `GET /models`.
 - **DeepSeek** — `DeepseekOpenAIClient` (`kind="chat_completions"`); models add `reasoning_content`, `prefix`, `ThinkingOptions`, and it injects the `thinking` flag (`enabled`, or `disabled` for `reasoning_effort = "none"`) after encoding, plus an empty `reasoning_content` on any assistant message that carries none (thinking mode refuses any round — a call or a plain answer — whose reasoning never came back). Config default model ids: `deepseek-flash`, `deepseek-v4-pro`. `DeepseekResponsesClient` (`kind="responses"`) extends the OpenAI Responses client for `convention = "responses"` (SSE stops at terminal events — DeepSeek sends no `[DONE]`). `DeepseekAnthropicClient` (`kind="messages"`) extends `AnthropicClient` for `convention = "anthropic"` (base `https://api.deepseek.com/anthropic`; `models()` returns `[]`).
 
@@ -78,8 +78,8 @@ Async SQLAlchemy + aiosqlite. `MemoryStore`: schema init (+ lightweight SQLite `
 
 - **Kind-based dispatch**: clients expose `kind` (`chat_completions` | `responses` | `messages`). Agent history stays chat-completions-shaped; transport adapters return synthetic `ChatCompletionResponse` / stream chunks.
 - **`responses_bridge` / `responses_dispatch`**: chat ↔ OpenAI Responses; stream text/reasoning deltas; tool calls + usage on `response.completed`; a request carrying `tools` returns every round's reasoning as a `reasoning` item of `reasoning_text` parts — "必须完整回传 `reasoning_content` … 即使该轮模型未实际进行工具调用" (a missing item and an empty part are both a 400, so a round that kept none sends a blank line) — and a round's `function_call` items stay contiguous ahead of their outputs (`call, output, call, output` was a 400: the second call reads as a round of its own without reasoning); reasoning is read back from the item's `content` parts.
-- **`messages_bridge` / `messages_dispatch`**: chat ↔ Anthropic Messages; system fold; tool_use/tool_result; stream text + tool argument fragments; `thinking` from the request's `reasoning_effort` (level → token budget, floor 1024, `max_tokens` grown when the budget would leave no room) plus the client's configured `thinking_budget_tokens`.
-- **Provider-side tools**: `OpenAIProvider.provider_tools` (list of dicts; default `[{type="web_search"}]` when omitted; `[]` disables) threaded via `ChatAgent`/`run_chat_loop` into Responses only; never executed by `ToolRegistry`.
+- **`messages_bridge` / `messages_dispatch`**: chat ↔ Anthropic Messages; system fold; tool_use/tool_result; stream text + tool argument fragments; `thinking` from the request's `reasoning_effort` (level → token budget, floor 1024, `max_tokens` grown when the budget would leave no room) plus the client's configured `thinking_budget_tokens`; a server tool (`server_tool_use` + its `web_search_tool_result`) is the surface's own business — it streams `input_json_delta` fragments like a call but nothing local asked for it, so only a `tool_use` block becomes a tool call, and the blocks are read (see `lmproto/anthropic/model.py`) but never executed.
+- **Provider-side tools**: `OpenAIProvider.provider_tools` (list of dicts; default `[{type="web_search"}]` when omitted; `[]` disables) threaded via `ChatAgent`/`run_chat_loop` into Responses and Messages; never executed by `ToolRegistry`. The Messages surface declares its own server tool instead — a bare `web_search` becomes `{type="web_search_20250305", name="web_search"}` (the search then runs inside the response, and its `encrypted_content` results must be passed back verbatim if they are kept at all).
 - **`@tool` / `ToolRegistry`**: decorator infers JSON Schema from type hints; execute tools by name. Before running, `execute` answers a call that omits a required argument with the accepted names (`_missing_required_error`) — the handler's bare `TypeError` never reaches the model, and no confirm prompt fires for a call that cannot run. Shape mistakes inside a *present* argument are the tool's own job (see the `chat` bullet).
 - **`run_chat_loop`**: multi-round tool loop; default **streaming** text deltas + stream tool-call merge; parallel tools; tool-result char budget; soft context compact on request (**API-calibrated** after first usage when available); cooperative cancel points; optional `on_limit`; resolved `reasoning_effort` on every request (see Config).
 - **`ChatAgent`**: optional `MemoryStore` (user message persisted immediately; **completed tool batches checkpointed** mid-turn; unfinished assistant suffix rolled back on failure); `stream`; system prompt; `provider_tools`; `retry()` continues incomplete turns (user-only **or** after committed tools — does not re-run those tools).