🐾 fix(mantle): survive Mantle's 20k output ceiling on tool-heavy turns

Two new Mantle shims, auto-installed alongside the existing pair:

- Opt out of the fine-grained-tool-streaming beta. Under it, an
  output-token cutoff mid-tool_use ends the stream without
  content_block_stop; fast-agent raises "Streaming completed but tool
  call never finished" and burns its retry ladder against the same
  wall (the observed ~700s Alan revise_workspace_file failures on
  Taurus).

- Clamp default maxTokens to Mantle's observed 20 000-token server
  ceiling, so the model stops gracefully (proper block close +
  stop_reason=max_tokens) instead of being cut off by the gateway.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

docs/bedrock.md

@@ -218,7 +218,7 @@ anthropic:
 
 That's the whole configuration. Pallas auto-detects the
 `bedrock-mantle` hostname in `anthropic.base_url` at startup and installs
-two compatibility shims so fast-agent's default request shape matches
+four compatibility shims so fast-agent's default request shape matches
 what Mantle expects (see `pallas/mantle_shims.py`):
 
 1. **Wire-name prefix** — re-adds the `anthropic.` prefix that fast-agent's
@@ -233,6 +233,25 @@ what Mantle expects (see `pallas/mantle_shims.py`):
    Input should be a valid dictionary or object"`, which would otherwise
    break the MCP tool-use loop on the second turn.
 
+3. **Fine-grained tool streaming opt-out** — stops fast-agent sending the
+   `fine-grained-tool-streaming-2025-05-14` beta. Under that beta an
+   output-token cutoff mid-`tool_use` block ends the stream without
+   `content_block_stop`, which fast-agent surfaces as
+   `Streaming completed but tool call never finished` and then retries
+   into the same wall (~700 s agent failures on large tool bodies).
+   Without the beta a cutoff closes blocks properly and lands in
+   fast-agent's graceful `stop_reason=max_tokens` handling.
+
+4. **`max_tokens` clamp** — Mantle enforces a server-side output ceiling
+   of 20 000 tokens per response regardless of the requested
+   `max_tokens` (fast-agent asks for the model's full 128 000). The shim
+   clamps default `maxTokens` to `MANTLE_MAX_OUTPUT_TOKENS` (20 000) so
+   the model stops gracefully at the limit instead of the gateway
+   cutting the stream. A single agent turn — thinking, prose, and tool
+   input combined — cannot exceed this on Mantle; agents that must emit
+   more output in one turn need to split the work (e.g. patch-style
+   edits instead of full-document rewrites).
+
 The Anthropic SDK appends `/v1/messages` to `base_url` automatically.
 
 **Feature support.** Mantle accepts the same Messages API request shape

docs/pallas.md

@@ -191,7 +191,7 @@ agents:
 | `agents.<name>.module` | yes | Importable Python module path containing a `fast` instance |
 | `agents.<name>.port` | yes | Port for this agent's StreamableHTTP MCP server |
 | `agents.<name>.title` | no | Display name in registry. Default: `name.title()` |
-| `agents.<name>.description` | no | Description in registry |
+| `agents.<name>.description` | no | Description in registry. Also becomes the `send_message` tool description, overriding any `description=` on the `@fast.agent` decorator |
 | `agents.<name>.model` | no | `provider.model-name` override for this agent. Overrides `default_model`, is applied to every agent in the module at startup, and is what the registry advertises for this entry |
 | `agents.<name>.model_capabilities` | no | Per-agent `{vision, context_window, max_output_tokens}` block. Overrides the top-level `model_capabilities`; the same defaults apply to omitted fields |
 | `agents.<name>.depends_on` | no | List of agent names that must start and become ready before this agent |
@@ -452,6 +452,16 @@ Each agent's MCP tool accepts:
 
 When `images` is provided, the message is sent as a `PromptMessageExtended` containing both `TextContent` and `ImageContent` parts — the agent's underlying model must support vision.
 
+#### Tool description
+
+The tool's description is what an MCP client shows next to the tool name, so it should say what the agent is *for*. Pallas resolves it in this order:
+
+1. `agents.<name>.description` from `agents.yaml` — the deployment's source of truth, and the same text published in the registry
+2. `description=` on the `@fast.agent` decorator
+3. `Send a message to the {agent} agent` — a generic fallback
+
+A description containing `{agent}` has the agent's name interpolated into it; other braces are left alone. Without (1), every agent whose module omits (2) falls through to the fallback, which tells a client nothing — so keep `agents.yaml` descriptions meaningful.
+
 ### Tool-Result Image Passthrough
 
 Images work in both directions. fast-agent's `agent.send()` returns only the final assistant text, so images produced by downstream tools during the agentic loop (playwright screenshots, rommie desktop captures) would otherwise reach the agent's own vision model but never the MCP caller. A per-request `after_tool_call` hook (`pallas.image_passthrough`) collects every `ImageContent` block from the turn's tool results; at end of turn `send_message` returns a `CallToolResult` whose content is the assistant's text block followed by the collected images. Turns that produce no images return the plain string, unchanged from previous releases.

pallas/mantle_shims.py

@@ -24,7 +24,23 @@ before the wire traffic is valid:
 
    Upstream SDK tracker: https://github.com/anthropics/anthropic-sdk-python/issues/1454
 
-Both shims are idempotent and may be installed at process startup before any
+3. **Fine-grained tool streaming truncation.** Fast-agent unconditionally
+   sends the ``fine-grained-tool-streaming-2025-05-14`` beta on tool-bearing
+   requests. Under that beta, an output-token cutoff mid-``tool_use`` block
+   ends the stream *without* ``content_block_stop``, which fast-agent's
+   stream accounting surfaces as ``Streaming completed but tool call never
+   finished`` — followed by a full retry ladder against the same wall
+   (observed as ~700 s agent failures on large ``revise_workspace_file``
+   bodies). We disable that one beta so a cutoff closes blocks properly and
+   lands in fast-agent's graceful ``stop_reason=max_tokens`` handling.
+
+4. **Output-token ceiling.** Mantle clamps ``max_tokens`` to 20 000
+   server-side (streams complete at exactly 20 000 output tokens regardless
+   of the requested 128 000). We clamp the request to that ceiling so the
+   *model* stops gracefully at the limit — emitting proper block-stop events
+   and ``stop_reason`` — instead of being cut off by the gateway's clamp.
+
+All shims are idempotent and may be installed at process startup before any
 fast-agent ``FastAgent`` instance is constructed.
 """
 from __future__ import annotations
@@ -129,12 +145,90 @@ def install_tool_use_caller_strip() -> None:
     logger.info("Mantle tool_use.caller strip shim installed")
 
 
+# ── Shim 3: disable fine-grained tool streaming ──────────────────────────────
+
+_beta_opt_out_installed = False
+
+
+def install_fine_grained_tool_streaming_opt_out() -> None:
+    """Stop fast-agent requesting the fine-grained tool streaming beta.
+
+    Under that beta a ``max_tokens`` cutoff mid-``tool_use`` ends the stream
+    without ``content_block_stop``; fast-agent then raises
+    ``Streaming completed but tool call never finished`` and burns its whole
+    retry ladder against the same ceiling. Without the beta the cutoff closes
+    blocks properly and fast-agent's ``stop_reason=max_tokens`` handling
+    applies. Safe to call more than once.
+    """
+    global _beta_opt_out_installed
+    if _beta_opt_out_installed:
+        return
+
+    from fast_agent.llm.provider.anthropic.llm_anthropic import AnthropicLLM
+
+    original_supports = AnthropicLLM.supports_direct_anthropic_beta
+
+    def patched_supports(self: Any, feature: str) -> bool:
+        if feature == "fine_grained_tool_streaming":
+            return False
+        return original_supports(self, feature)
+
+    AnthropicLLM.supports_direct_anthropic_beta = patched_supports  # type: ignore[method-assign]
+
+    _beta_opt_out_installed = True
+    logger.info("Mantle fine-grained tool streaming opt-out shim installed")
+
+
+# ── Shim 4: clamp max_tokens to Mantle's output ceiling ──────────────────────
+
+# Observed server-side clamp: Mantle streams stop at exactly 20 000 output
+# tokens however large the requested max_tokens. Requesting the ceiling
+# explicitly makes the model stop gracefully (proper block close + stop_reason)
+# instead of the gateway cutting the stream at its own limit.
+MANTLE_MAX_OUTPUT_TOKENS = 20_000
+
+_max_tokens_clamp_installed = False
+
+
+def install_max_tokens_clamp() -> None:
+    """Clamp default ``maxTokens`` to Mantle's output ceiling.
+
+    Wraps ``AnthropicLLM._initialize_default_params`` so every agent's
+    default request params carry an explicit ``maxTokens`` no higher than the
+    ceiling. Per-request ``RequestParams`` overrides bypass this — none of
+    our agent modules set one. Safe to call more than once.
+    """
+    global _max_tokens_clamp_installed
+    if _max_tokens_clamp_installed:
+        return
+
+    from fast_agent.llm.provider.anthropic.llm_anthropic import AnthropicLLM
+
+    original_init = AnthropicLLM._initialize_default_params  # noqa: SLF001
+
+    def patched_init(self: Any, kwargs: dict) -> Any:
+        params = original_init(self, kwargs)
+        if params.maxTokens is None or params.maxTokens > MANTLE_MAX_OUTPUT_TOKENS:
+            params.maxTokens = MANTLE_MAX_OUTPUT_TOKENS
+        return params
+
+    AnthropicLLM._initialize_default_params = patched_init  # noqa: SLF001
+
+    _max_tokens_clamp_installed = True
+    logger.info(
+        "Mantle max_tokens clamp shim installed (ceiling %d)",
+        MANTLE_MAX_OUTPUT_TOKENS,
+    )
+
+
 # ── Orchestrator ─────────────────────────────────────────────────────────────
 
 def install_all() -> None:
     """Install all Mantle shims. Call once at process startup."""
     install_wire_name_prefix()
     install_tool_use_caller_strip()
+    install_fine_grained_tool_streaming_opt_out()
+    install_max_tokens_clamp()
 
 
 def maybe_install(anthropic_base_url: str | None) -> bool:

pallas/server.py

@@ -283,12 +283,26 @@ async def _start_agent(name: str, agents: dict[str, dict]) -> None:
             if entry.get(k) is not None
         }
 
+        # The agents.yaml description is the one an operator actually wrote,
+        # and it is already published in the registry.  Reuse it as the
+        # `send_message` tool description so MCP clients see something
+        # meaningful instead of the "Send a message to the {agent} agent"
+        # fallback.
+        #
+        # Precedence, per register_agent_tools: this value wins over a
+        # `description=` on the @fast.agent decorator, which in turn wins over
+        # the generic fallback.  agents.yaml is the deployment's source of
+        # truth for agent metadata, so an operator editing it should not be
+        # silently overridden by a value buried in the agent module.
+        tool_description = entry.get("description") or None
+
         server = MultimodalAgentMCPServer(
             primary_instance=primary_instance,
             create_instance=fast_instance._server_instance_factory,
             dispose_instance=fast_instance._server_instance_dispose,
             instance_scope="request",
             server_name=f"{fast_instance.name}-MCP-Server",
+            tool_description=tool_description,
             host="0.0.0.0",
             get_registry_version=fast_instance._get_registry_version,
             request_limits=request_limits,

tests/test_mantle_shims.py

@@ -114,34 +114,97 @@ def test_install_tool_use_caller_strip_is_idempotent() -> None:
     mantle_shims.install_tool_use_caller_strip()  # must not raise or re-wrap
 
 
+# ── install_fine_grained_tool_streaming_opt_out ──────────────────────────────
+
+def test_fine_grained_beta_opt_out() -> None:
+    from fast_agent.llm.provider.anthropic.llm_anthropic import AnthropicLLM
+
+    mantle_shims.install_fine_grained_tool_streaming_opt_out()
+
+    # The patched method never touches self, so a bare object suffices.
+    stub = object()
+    assert AnthropicLLM.supports_direct_anthropic_beta(stub, "fine_grained_tool_streaming") is False
+    # Every other beta keeps the base-class answer (True).
+    assert AnthropicLLM.supports_direct_anthropic_beta(stub, "interleaved_thinking") is True
+    assert AnthropicLLM.supports_direct_anthropic_beta(stub, "long_context") is True
+
+
+def test_fine_grained_beta_opt_out_is_idempotent() -> None:
+    mantle_shims.install_fine_grained_tool_streaming_opt_out()
+    mantle_shims.install_fine_grained_tool_streaming_opt_out()  # must not re-wrap
+
+    from fast_agent.llm.provider.anthropic.llm_anthropic import AnthropicLLM
+
+    assert (
+        AnthropicLLM.supports_direct_anthropic_beta(object(), "fine_grained_tool_streaming")
+        is False
+    )
+
+
+# ── install_max_tokens_clamp ─────────────────────────────────────────────────
+
+@pytest.mark.parametrize(
+    "initial,expected",
+    [
+        (128000, mantle_shims.MANTLE_MAX_OUTPUT_TOKENS),  # over the ceiling → clamped
+        (None, mantle_shims.MANTLE_MAX_OUTPUT_TOKENS),    # unset → pinned to ceiling
+        (4096, 4096),                                     # under the ceiling → untouched
+    ],
+)
+def test_max_tokens_clamp(
+    monkeypatch: pytest.MonkeyPatch, initial: int | None, expected: int
+) -> None:
+    from fast_agent.llm.provider.anthropic.llm_anthropic import AnthropicLLM
+    from fast_agent.types import RequestParams
+
+    # Stub the underlying initializer, then force a fresh wrap around it.
+    monkeypatch.setattr(
+        AnthropicLLM,
+        "_initialize_default_params",
+        lambda self, kwargs: RequestParams(maxTokens=initial),
+    )
+    monkeypatch.setattr(mantle_shims, "_max_tokens_clamp_installed", False)
+    mantle_shims.install_max_tokens_clamp()
+
+    params = AnthropicLLM._initialize_default_params(object(), {})
+    assert params.maxTokens == expected
+
+
+def test_max_tokens_clamp_is_idempotent() -> None:
+    mantle_shims.install_max_tokens_clamp()
+    mantle_shims.install_max_tokens_clamp()  # must not raise or re-wrap
+
+
 # ── maybe_install ────────────────────────────────────────────────────────────
 
-def test_maybe_install_installs_when_mantle(monkeypatch: pytest.MonkeyPatch) -> None:
+_INSTALLER_NAMES = [
+    ("install_wire_name_prefix", "wire"),
+    ("install_tool_use_caller_strip", "tool_use"),
+    ("install_fine_grained_tool_streaming_opt_out", "beta_opt_out"),
+    ("install_max_tokens_clamp", "max_tokens"),
+]
+
+
+def _patch_installers(monkeypatch: pytest.MonkeyPatch) -> list[str]:
     calls: list[str] = []
-    monkeypatch.setattr(
+    for attr, label in _INSTALLER_NAMES:
-        mantle_shims, "install_wire_name_prefix",
+        monkeypatch.setattr(
-        lambda: calls.append("wire"),
+            mantle_shims, attr,
-    )
+            lambda label=label: calls.append(label),
-    monkeypatch.setattr(
+        )
-        mantle_shims, "install_tool_use_caller_strip",
+    return calls
-        lambda: calls.append("tool_use"),
+
-    )
+
+def test_maybe_install_installs_when_mantle(monkeypatch: pytest.MonkeyPatch) -> None:
+    calls = _patch_installers(monkeypatch)
 
     installed = mantle_shims.maybe_install("https://bedrock-mantle.us-east-1.api.aws/anthropic")
     assert installed is True
-    assert calls == ["wire", "tool_use"]
+    assert calls == ["wire", "tool_use", "beta_opt_out", "max_tokens"]
 
 
 def test_maybe_install_noop_for_non_mantle(monkeypatch: pytest.MonkeyPatch) -> None:
-    calls: list[str] = []
+    calls = _patch_installers(monkeypatch)
-    monkeypatch.setattr(
-        mantle_shims, "install_wire_name_prefix",
-        lambda: calls.append("wire"),
-    )
-    monkeypatch.setattr(
-        mantle_shims, "install_tool_use_caller_strip",
-        lambda: calls.append("tool_use"),
-    )
 
     assert mantle_shims.maybe_install("https://api.anthropic.com") is False
     assert mantle_shims.maybe_install(None) is False

tests/test_tool_description.py

@@ -0,0 +1,88 @@
+"""Tests for send_message tool-description resolution.
+
+``server._start_agent`` passes the agents.yaml ``description`` through as
+``tool_description``, so MCP clients see the description an operator actually
+wrote instead of the generic "Send a message to the {agent} agent" fallback.
+
+These pin the precedence implemented in
+``MultimodalAgentMCPServer.register_agent_tools``:
+
+    tool_description (agents.yaml)  >  @fast.agent description  >  fallback
+
+Constructing a real ``MultimodalAgentMCPServer`` needs a live FastAgent
+instance, so these exercise the resolution expression directly — it is the
+part that carries the logic, and the part that would silently regress.
+"""
+from __future__ import annotations
+
+import pytest
+
+
+# ── Helpers ──────────────────────────────────────────────────────────────────
+
+
+def _resolve(
+    tool_description: str | None,
+    agent_description: str | None = None,
+    agent_name: str = "scotty",
+) -> str:
+    """Mirror register_agent_tools' description resolution."""
+    resolved = (
+        tool_description.format(agent=agent_name)
+        if tool_description and "{agent}" in tool_description
+        else tool_description
+    )
+    return (
+        resolved
+        or agent_description
+        or f"Send a message to the {agent_name} agent"
+    )
+
+
+# ── Precedence ───────────────────────────────────────────────────────────────
+
+
+def test_agents_yaml_description_is_used() -> None:
+    """The agents.yaml description reaches the tool, not the fallback."""
+    assert _resolve("Systems administration expert", None) == (
+        "Systems administration expert"
+    )
+
+
+def test_agents_yaml_wins_over_decorator() -> None:
+    """agents.yaml is the deployment's source of truth for agent metadata."""
+    assert _resolve("From agents.yaml", "From the decorator") == "From agents.yaml"
+
+
+def test_decorator_used_when_no_yaml_description() -> None:
+    """An agent module's own description still beats the generic fallback."""
+    assert _resolve(None, "From the decorator") == "From the decorator"
+
+
+def test_falls_back_when_nothing_configured() -> None:
+    """With neither source set, the generic fallback stands."""
+    assert _resolve(None, None) == "Send a message to the scotty agent"
+
+
+@pytest.mark.parametrize("empty", ["", None])
+def test_empty_description_falls_through(empty: str | None) -> None:
+    """An empty agents.yaml description must not shadow the other sources."""
+    assert _resolve(empty, "From the decorator") == "From the decorator"
+
+
+# ── Templating ───────────────────────────────────────────────────────────────
+
+
+def test_agent_placeholder_is_interpolated() -> None:
+    """``{agent}`` in a description is substituted with the agent name."""
+    assert _resolve("Talk to {agent} about ops") == "Talk to scotty about ops"
+
+
+def test_other_braces_are_left_alone() -> None:
+    """Prose containing braces is only formatted when it holds ``{agent}``.
+
+    Guards the ``"{agent}" in ...`` check — an unconditional ``.format()``
+    would raise KeyError on a description mentioning JSON.
+    """
+    description = 'Handles JSON like {"a": 1} safely'
+    assert _resolve(description, None) == description