Update claude-api skill: Managed Agents July launch wave, partner pricing, tool-runner corrections (#1463)

Syncs the claude-api skill with the current upstream source.

## Managed Agents — five new features

- `effort` on the agent's `model` object (a level string or `{"type": "<level>"}`).
  It is agent-configuration only: setting it in a per-session `model` override is
  silently ignored.
- Optional `version` on agent update, for optimistic concurrency. Omit it for
  last-write-wins.
- `initial_events` on session create, collapsing create plus first send into one
  call. Validation is all-or-nothing and only `user.message` and
  `user.define_outcome` are accepted.
- Environment and memory-store webhooks: four `environment.*` events and three
  `memory_store.*` events.
- Event deltas on per-thread streams. Previews are thread-scoped, so a child
  thread's previews never reach the session-level stream.

## Corrections to behavior the skill already documented

- Tool output offload triggers at 100,000 characters (~25k tokens), not 100K
  tokens, and covers built-in tools rather than MCP alone.
- `system.message` works on four models, checks only the primary model, appends
  system context instead of replacing the prompt, and is accepted during a
  `requires_action` idle when it trails a tool result in the same request.
- Vault-to-MCP credential matching is normalized (scheme and host lowercased,
  default ports and trailing slashes stripped), not byte-exact.
- Multiagent depth greater than 1 is a validation error, not silently ignored.
- Outcome `interrupted` fires even when evaluation never started, and then
  carries an empty-string `outcome_evaluation_start_id`.
- Deployment jitter is 15% of the run interval, floor 5s, cap 9 minutes.
- Webhooks retry three times with jittered 5-120s backoff, then drop silently.
  Auto-disable is duration-based with three named triggers.
- `session.status_terminated` means completion or error.
- `agent.thinking` is a progress signal and carries no thinking content.
- `processed_at` is already populated on first sighting for
  `user.define_outcome`, `user.custom_tool_result`, and `user.tool_result`.
- Session creation does not provision the sandbox.
- Skill `version` applies to Anthropic-authored skills too.
- Console-created vault credentials are header-injection only, and vault
  environment-variable substitution skips secrets in URL paths.
- Console trace URLs need the real workspace ID when the API key is not in the
  Default workspace.

## Elsewhere in the skill

- Note partner pricing under the first-party price table: Microsoft Foundry
  bills at standard API rates, while Amazon Bedrock and Vertex AI are
  partner-operated with separate pricing.
- Correct the tool-runner human-in-the-loop guidance, which previously pointed
  readers at the manual loop for approval gates the runner's per-turn hooks
  already cover.
- Repoint links away from the retired documentation hosts.
- Dedupe eagerly loaded SKILL.md content that the per-section files already
  cover.

skills/claude-api/python/claude-api/streaming.md

@@ -73,7 +73,7 @@ with client.messages.stream(
 
 ## Streaming with Tool Use
 
-The Python tool runner currently returns complete messages. Use streaming for individual API calls within a manual loop if you need per-token streaming with tools:
+The Python tool runner supports streaming: pass `stream=True` to `client.beta.messages.tool_runner(...)` and each iteration yields a stream you consume event-by-event, with `get_final_message()` for the accumulated message per turn (see `shared/tool-use-concepts.md` → Tool Runner vs Manual Loop). Use the manual-loop pattern below only when you're not using the tool runner and need per-token streaming with tools:
 
 ```python
 with client.messages.stream(

skills/claude-api/python/claude-api/tool-use.md

@@ -47,6 +47,43 @@ For async usage, use `@beta_async_tool` with `async def` functions.
 - Tool schemas are generated automatically from function signatures
 - Iteration stops automatically when Claude has no more tool calls
 
+### Server tools with the tool runner
+
+The runner's `tools` list accepts raw server-tool definitions (`web_search_20260209`, `web_fetch_20260209`, code execution) alongside decorated tools — pass the literal tool dict; server tools run on Anthropic's servers, so there is no function to implement.
+
+**Caution — the runner does not auto-resume `pause_turn` (as of `anthropic` 0.116.0).** A long-running server-tool turn can stop with `stop_reason: "pause_turn"`. The runner only continues after a client tool produces a result, so a paused turn ends the loop and is returned as the final message — no error, no warning, just a silently truncated answer. Unlike the TypeScript runner, the Python runner cannot be resumed mid-loop: it exits unconditionally when no client tool ran, and `runner.append_messages(...)` does not prevent the exit. To handle `pause_turn`, mirror the conversation history as you iterate, then restart the runner with the paused turn appended:
+
+```python
+messages = [{"role": "user", "content": user_input}]
+
+max_restarts = 5  # cap pause_turn restarts, mirroring max_continuations advice
+restarts = 0
+while True:
+    runner = client.beta.messages.tool_runner(
+        model="claude-opus-4-8",
+        max_tokens=16000,
+        tools=tools,  # may mix @beta_tool functions and server-tool definitions
+        messages=messages,
+    )
+    last = None
+    for message in runner:
+        last = message
+        # Mirror the history — the runner keeps its own copy and does not expose it
+        messages.append({"role": "assistant", "content": message.content})
+        tool_response = runner.generate_tool_call_response()  # cached; tools still run once
+        if tool_response is not None:
+            messages.append(tool_response)
+    if last is None or last.stop_reason != "pause_turn":
+        break
+    restarts += 1
+    if restarts > max_restarts:
+        raise RuntimeError("giving up: turn still paused after max_restarts")
+    # Paused mid-turn: `messages` already ends with the paused assistant
+    # turn, so the next runner resumes it
+```
+
+Alternatively, use the manual loop below, which handles `pause_turn` explicitly.
+
 ---
 
 ## MCP Tool Conversion Helpers
@@ -130,7 +167,9 @@ Conversion functions raise `UnsupportedMCPValueError` if an MCP value cannot be
 
 ## Manual Agentic Loop
 
-Use this when you need fine-grained control over the loop (e.g., custom logging, conditional tool execution, human-in-the-loop approval):
+Prefer the tool runner above. Drop to a manual loop only when you need control the runner does not expose (e.g., a custom transport, request shapes the SDK cannot build, or avoiding a beta dependency — the runner is beta). Human-in-the-loop approval does *not* require a manual loop — gate inside the tool function (return a "user declined" result) or inspect pending `tool_use` blocks in the `for message in runner:` body and call `runner.set_messages_params()`.
+
+If you do need a manual loop:
 
 ```python
 import anthropic