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fix(tools): Refactor tool filtering into a single assembly pass (#7950)

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* Refractor the tools filtering logic.

* Address the feedback.
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MubashirAziz1 2026-11-07 19:33:59 +05:00 committed by GitHub
parent f68abbb831
commit d1238c6974
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1 changed files with 83 additions and 61 deletions
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144
backend/packages/harness/deerflow/tools/tools.py
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@ -1,4 +1,5 @@
import copy
import itertools
import logging
import threading
@ -8,6 +9,7 @@ from pydantic import BaseModel
from deerflow.config import get_app_config
from deerflow.config.app_config import AppConfig
from deerflow.config.tool_config import ToolConfig
from deerflow.constants import CONVERSATION_TOOL_USE
from deerflow.mcp.tasks.runtime import is_mcp_task_runtime_available
from deerflow.reflection import resolve_variable
@ -43,6 +45,15 @@ SUBAGENT_TOOLS = [
# task_status_tool is no longer exposed to LLM (backend handles polling internally)
]
# These operations are assembled from the host grant in get_available_tools().
# Registering a tool path in YAML cannot widen that grant or its interaction mode.
_HOST_GRANTED_TOOL_USES = frozenset(
{
"deerflow.tools.scheduled_tasks:schedule_task",
"deerflow.tools.scheduled_tasks:stop_scheduled_task",
}
)
def _is_host_bash_tool(tool: object) -> bool:
"""Return True if the tool config represents a host-bash execution surface."""
@ -102,6 +113,53 @@ def _clone_tool_with_description(tool: BaseTool, description: str) -> BaseTool:
return cloned
def _select_tool_configs(config: AppConfig, groups: list[str] | None, include_conversation_reader: bool) -> list[ToolConfig]:
"""Return the configured tool entries this assembly may expose, in config order.
Every exclusion rule is a pure per-entry predicate, so they are evaluated in
a single pass with the config-wide decisions computed once up front.
"""
group_filter = None if groups is None else set(groups)
knowledge_enabled = bool(getattr(getattr(config, "knowledge_base", None), "enabled", False))
host_bash_allowed = is_host_bash_allowed(config)
selected: list[ToolConfig] = []
for tool in config.tools:
if group_filter is not None and tool.group not in group_filter:
continue
if tool.use in _HOST_GRANTED_TOOL_USES:
continue
if not include_conversation_reader and tool.use == CONVERSATION_TOOL_USE:
continue
# Knowledge tools are opt-in as a group. Provider connection and retrieval
# settings live on each tool entry; the generic capability flag controls
# whether the group is exposed at all.
if not knowledge_enabled and tool.group == "knowledge":
continue
# Do not expose host bash by default when LocalSandboxProvider is active.
if not host_bash_allowed and _is_host_bash_tool(tool):
continue
selected.append(tool)
return selected
def _write_file_budget_note(max_tokens: int | None) -> str | None:
"""Return the write_file per-response budget hint, or None when no usable cap exists."""
if max_tokens is None:
return None
try:
safe_chars = int(max_tokens * 3 * 0.7)
except OverflowError:
# Even finite caps can be too large for this optional estimate.
return None
return (
f"\n\nPER-RESPONSE BUDGET: your output limit is {max_tokens} tokens "
f"(≈{safe_chars} chars). Single non-append writes above this will be truncated. "
"For larger documents, write the first section now, "
"then use append=True for subsequent sections."
)
def get_available_tools(
groups: list[str] | None = None,
include_mcp: bool = True,
@ -144,31 +202,26 @@ def get_available_tools(
List of available tools.
"""
config = app_config or get_app_config()
tool_configs = [tool for tool in config.tools if groups is None or tool.group in groups]
# These operations are assembled from the host grant below. Registering a
# tool path in YAML cannot widen that grant or its interaction mode.
tool_configs = [tool for tool in tool_configs if tool.use not in {"deerflow.tools.scheduled_tasks:schedule_task", "deerflow.tools.scheduled_tasks:stop_scheduled_task"}]
if not include_conversation_reader:
tool_configs = [tool for tool in tool_configs if tool.use != CONVERSATION_TOOL_USE]
# Knowledge tools are opt-in as a group. Provider connection and retrieval
# settings live on each tool entry; the generic capability flag controls
# whether the group is exposed at all.
knowledge_base_config = getattr(config, "knowledge_base", None)
if not getattr(knowledge_base_config, "enabled", False):
tool_configs = [tool for tool in tool_configs if tool.group != "knowledge"]
# If no model_name specified, use the first model (default)
if model_name is None and config.models:
model_name = config.models[0].name
model_config = config.get_model_config(model_name) if model_name else None
# Do not expose host bash by default when LocalSandboxProvider is active.
if not is_host_bash_allowed(config):
tool_configs = [tool for tool in tool_configs if not _is_host_bash_tool(tool)]
# Annotate write_file with the constructed model's effective output budget so the
# model does not assume the 80 KB streaming ceiling is the practical limit
# for a single completion. The tool is cloned to avoid mutating the
# module-level singleton in-place across assemblies or leaking guidance to
# models configured without max_tokens.
budget_note = _write_file_budget_note(_extract_max_tokens(chat_model if chat_model is not None else model_config))
loaded_tools_raw = [(cfg, resolve_variable(cfg.use, BaseTool)) for cfg in tool_configs]
# Warn when the config ``name`` field and the tool object's ``.name``
# attribute diverge — this mismatch is the root cause of issue #1803 where
# the LLM receives one name in its tool schema but the runtime router
# recognises a different name, producing "not a valid tool" errors.
for cfg, loaded in loaded_tools_raw:
loaded_tools: list[BaseTool] = []
for cfg in _select_tool_configs(config, groups, include_conversation_reader):
loaded = resolve_variable(cfg.use, BaseTool)
# Warn when the config ``name`` field and the tool object's ``.name``
# attribute diverge — this mismatch is the root cause of issue #1803 where
# the LLM receives one name in its tool schema but the runtime router
# recognises a different name, producing "not a valid tool" errors.
if cfg.name != loaded.name:
logger.warning(
"Tool name mismatch: config name %r does not match tool .name %r (use: %s). The tool's own .name will be used for binding.",
@ -176,8 +229,12 @@ def get_available_tools(
loaded.name,
cfg.use,
)
loaded_tools = [_ensure_sync_invocable_tool(t) for _, t in loaded_tools_raw]
loaded = _ensure_sync_invocable_tool(loaded)
if budget_note is not None and loaded.name == "write_file" and hasattr(loaded, "description"):
description = getattr(loaded, "description", "") or ""
if "PER-RESPONSE BUDGET:" not in description:
loaded = _clone_tool_with_description(loaded, f"{description}{budget_note}")
loaded_tools.append(loaded)
# Conditionally add tools based on config
builtin_tools = BUILTIN_TOOLS.copy()
@ -205,46 +262,12 @@ def get_available_tools(
builtin_tools.extend(bind_batch_tools(submitter_provider=get_subagent_batch_submitter, app_config=config))
logger.info("Including native subagent tools")
# If no model_name specified, use the first model (default)
if model_name is None and config.models:
model_name = config.models[0].name
# Add view_image_tool only if the model supports vision
model_config = config.get_model_config(model_name) if model_name else None
builtin_tools.append(build_inspect_run_status_tool(context_window=model_config.context_window if model_config else None))
if model_config is not None and model_config.supports_vision:
builtin_tools.append(view_image_tool)
logger.info(f"Including view_image_tool for model '{model_name}' (supports_vision=True)")
# Annotate write_file with the constructed model's effective output budget so the
# model does not assume the 80 KB streaming ceiling is the practical limit
# for a single completion. The tool is cloned to avoid mutating the
# module-level singleton in-place across assemblies or leaking guidance to
# models configured without max_tokens.
max_tokens = _extract_max_tokens(chat_model if chat_model is not None else model_config)
if max_tokens is not None:
try:
safe_chars = int(max_tokens * 3 * 0.7)
except OverflowError:
# Even finite caps can be too large for this optional estimate.
pass
else:
budget_note = (
f"\n\nPER-RESPONSE BUDGET: your output limit is {max_tokens} tokens "
f"(≈{safe_chars} chars). Single non-append writes above this will be truncated. "
"For larger documents, write the first section now, "
"then use append=True for subsequent sections."
)
loaded_tools = [
_clone_tool_with_description(
tool,
f"{getattr(tool, 'description', '') or ''}{budget_note}",
)
if tool.name == "write_file" and hasattr(tool, "description") and "PER-RESPONSE BUDGET:" not in (getattr(tool, "description", "") or "")
else tool
for tool in loaded_tools
]
# Get cached MCP tools if enabled
# NOTE: We use ExtensionsConfig.from_file() instead of config.extensions
# to always read the latest configuration from disk. This ensures that changes
@ -338,14 +361,13 @@ def get_available_tools(
from deerflow.extensions import get_agent_build_extensions
from deerflow.extensions.plugin_tools import build_plugin_tools
ordinary_tools = loaded_tools + builtin_tools + mcp_tools + acp_tools
# Keep plugin-vs-plugin validation strict. Host/plugin collisions use the
# ordinary-first deduplication below, without dropping unrelated tools.
plugin_tools = build_plugin_tools(extensions if extensions is not None else get_agent_build_extensions(), groups=groups)
all_tools = [_ensure_sync_invocable_tool(t) for t in ordinary_tools + plugin_tools]
seen_names: set[str] = set()
unique_tools: list[BaseTool] = []
for t in all_tools:
for t in itertools.chain(loaded_tools, builtin_tools, mcp_tools, acp_tools, plugin_tools):
t = _ensure_sync_invocable_tool(t)
if t.name not in seen_names:
unique_tools.append(t)
seen_names.add(t.name)