deer-flow/backend/packages/harness/deerflow/tools/builtins/tool_search.py

"""Tool search — deferred tool discovery at runtime.

Contains:
- DeferredToolCatalog: immutable, searchable catalog of deferred tools.
- build_tool_search_tool: builds the `tool_search` tool as a closure over a
  catalog; it records promotions into graph state via ``Command``.
- build_deferred_tool_setup: assembles the catalog + tool from a
  policy-filtered tool list (call AFTER tool-policy filtering).

The agent sees deferred tool names in <available-deferred-tools> but cannot
call them until it fetches their full schema via the tool_search tool. The
deferred set rides on a build-time closure and promotion lives in per-thread
graph state — there is no ContextVar. Source-agnostic: a tool is "deferred"
when it carries the ``deerflow_mcp`` metadata tag.
"""

import hashlib
import json
import logging
import re
from dataclasses import dataclass
from functools import cached_property
from typing import Annotated

from langchain.tools import BaseTool
from langchain_core.messages import ToolMessage
from langchain_core.tools import InjectedToolCallId, tool
from langchain_core.utils.function_calling import convert_to_openai_function
from langgraph.types import Command

from deerflow.tools.mcp_metadata import is_mcp_tool

logger = logging.getLogger(__name__)

MAX_RESULTS = 5  # Max tools returned per search


def _compile_catalog_regex(pattern: str) -> re.Pattern[str]:
    """Compile ``pattern`` case-insensitively, falling back to a literal match.

    Search queries come from the model, so an invalid regex (e.g. an unbalanced
    paren) must degrade to a literal substring match rather than raise.
    """
    try:
        return re.compile(pattern, re.IGNORECASE)
    except re.error:
        return re.compile(re.escape(pattern), re.IGNORECASE)


# ── Catalog ──


# NOTE: frozen=True without slots=True keeps __dict__, which is what lets the
# @cached_property fields below cache (they write to instance.__dict__, bypassing
# the frozen __setattr__). Do NOT add slots=True or hash/names break at runtime.
@dataclass(frozen=True)
class DeferredToolCatalog:
    """Immutable catalog of deferred tools. Pure search, no mutation."""

    tools: tuple[BaseTool, ...]

    @cached_property
    def names(self) -> frozenset[str]:
        return frozenset(t.name for t in self.tools)

    @cached_property
    def hash(self) -> str:
        canon = [{"name": t.name, "schema": convert_to_openai_function(t)} for t in sorted(self.tools, key=lambda t: t.name)]
        blob = json.dumps(canon, sort_keys=True, ensure_ascii=False, default=str)
        return hashlib.sha256(blob.encode("utf-8")).hexdigest()[:16]

    def search(self, query: str) -> list[BaseTool]:
        query = query.strip()
        if not query:
            return []

        if query.startswith("select:"):
            wanted = {n.strip() for n in query[7:].split(",")}
            return [t for t in self.tools if t.name in wanted][:MAX_RESULTS]

        if query.startswith("+"):
            parts = query[1:].split(None, 1)
            if not parts:
                return []  # bare "+" with no required token — nothing to require
            required = parts[0].lower()
            candidates = [t for t in self.tools if required in t.name.lower()]
            if len(parts) > 1:
                candidates.sort(key=lambda t: _catalog_regex_score(parts[1], t), reverse=True)
            return candidates[:MAX_RESULTS]

        regex = _compile_catalog_regex(query)
        scored: list[tuple[int, BaseTool]] = []
        for t in self.tools:
            searchable = f"{t.name} {t.description or ''}"
            if regex.search(searchable):
                scored.append((2 if regex.search(t.name) else 1, t))
        scored.sort(key=lambda x: x[0], reverse=True)
        return [t for _, t in scored][:MAX_RESULTS]


def _catalog_regex_score(pattern: str, t: BaseTool) -> int:
    regex = _compile_catalog_regex(pattern)
    return len(regex.findall(f"{t.name} {t.description or ''}"))


# ── Setup / tool ──


@dataclass(frozen=True)
class DeferredToolSetup:
    """Result of assembling deferred-tool support for one agent build.

    The three fields move as a unit, so callers branch on ``tool_search_tool``:

    - **Empty** ``(None, frozenset(), None)``: deferral is disabled, or no MCP
      tool survived policy filtering. Nothing is deferred — bind tools as-is.
    - **Populated**: ``tool_search_tool`` is appended to the agent's tools,
      ``deferred_names`` are withheld from the model until promoted, and
      ``catalog_hash`` scopes those promotions in graph state.

    Invariant: ``tool_search_tool is None`` ⟺ ``deferred_names`` is empty ⟺
    ``catalog_hash is None``.
    """

    tool_search_tool: BaseTool | None
    deferred_names: frozenset[str]
    catalog_hash: str | None


def build_tool_search_tool(catalog: DeferredToolCatalog) -> BaseTool:
    catalog_hash = catalog.hash

    @tool
    def tool_search(query: str, tool_call_id: Annotated[str, InjectedToolCallId]) -> Command:
        """Fetches full schema definitions for deferred tools so they can be called.

        Deferred tools appear by name in <available-deferred-tools> in the system
        prompt. Until fetched, only the name is known. This tool matches a query
        against the deferred tools and returns the matched tools complete schemas;
        once returned, a tool becomes callable.

        Query forms:
          - "select:Read,Edit" -- fetch these exact tools by name
          - "notebook jupyter" -- keyword search, up to max_results best matches
          - "+slack send" -- require "slack" in the name, rank by remaining terms
        """
        matched = catalog.search(query)[:MAX_RESULTS]
        if not matched:
            content, names = f"No tools found matching: {query}", []
        else:
            content = json.dumps([convert_to_openai_function(t) for t in matched], indent=2, ensure_ascii=False)
            names = [t.name for t in matched]
        return Command(
            update={
                "promoted": {"catalog_hash": catalog_hash, "names": names},
                "messages": [ToolMessage(content=content, tool_call_id=tool_call_id, name="tool_search")],
            }
        )

    return tool_search


def build_deferred_tool_setup(filtered_tools: list[BaseTool], *, enabled: bool) -> DeferredToolSetup:
    """Build the deferred-tool setup from a POLICY-FILTERED tool list.

    Must be called after skill/agent tool-policy filtering so the catalog never
    exposes a tool the current agent is not allowed to use.

    Returns an empty setup (see :class:`DeferredToolSetup`) in two distinct
    cases: deferral is disabled, or it is enabled but no MCP tool survived
    filtering.
    """
    if not enabled:
        # Deferral disabled: defer nothing; the model binds every tool as before.
        return DeferredToolSetup(None, frozenset(), None)
    deferred = [t for t in filtered_tools if is_mcp_tool(t)]
    if not deferred:
        # Enabled, but no MCP tool to defer: same empty result, different reason.
        return DeferredToolSetup(None, frozenset(), None)
    catalog = DeferredToolCatalog(tuple(deferred))
    return DeferredToolSetup(build_tool_search_tool(catalog), catalog.names, catalog.hash)