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https://github.com/bytedance/deer-flow.git
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f9340c1f08
* test(mcp): cover passive skill tool visibility * test(mcp): tighten deferred discovery coverage
342 lines
14 KiB
Python
342 lines
14 KiB
Python
"""Tool search — deferred tool discovery at runtime.
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Contains:
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- DeferredToolCatalog: immutable, searchable catalog of deferred tools.
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- build_tool_search_tool: builds the `tool_search` tool as a closure over a
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catalog; it records promotions into graph state via ``Command``.
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- build_deferred_tool_setup: assembles the catalog + tool from the tools
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configured for this agent build.
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- build_mcp_routing_middleware: builds the PR2 auto-promote middleware from
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serialized routing metadata on deferred tools available to the caller.
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The agent sees deferred tool names in <available-deferred-tools> but cannot
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call them until it fetches their full schema via the tool_search tool. The
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deferred set rides on a build-time closure and promotion lives in per-thread
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graph state — there is no ContextVar. Source-agnostic: a tool is "deferred"
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when it carries the ``deerflow_mcp`` metadata tag.
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"""
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import hashlib
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import html
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import json
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import logging
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import re
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from collections.abc import Iterable
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from dataclasses import dataclass
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from functools import cached_property
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from typing import TYPE_CHECKING, Annotated, Any
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from langchain.tools import BaseTool
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from langchain_core.messages import ToolMessage
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from langchain_core.tools import InjectedToolCallId, tool
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from langchain_core.utils.function_calling import convert_to_openai_function
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from langgraph.types import Command
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from deerflow.tools.mcp_metadata import get_mcp_routing, is_mcp_tool
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if TYPE_CHECKING:
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from langchain.agents.middleware import AgentMiddleware
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logger = logging.getLogger(__name__)
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MAX_RESULTS = 5 # Max tools returned per search
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def _compile_catalog_regex(pattern: str) -> re.Pattern[str]:
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"""Compile ``pattern`` case-insensitively, falling back to a literal match.
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Search queries come from the model, so an invalid regex (e.g. an unbalanced
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paren) must degrade to a literal substring match rather than raise.
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"""
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try:
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return re.compile(pattern, re.IGNORECASE)
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except re.error:
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return re.compile(re.escape(pattern), re.IGNORECASE)
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# ── Catalog ──
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# NOTE: frozen=True without slots=True keeps __dict__, which is what lets the
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# @cached_property fields below cache (they write to instance.__dict__, bypassing
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# the frozen __setattr__). Do NOT add slots=True or hash/names break at runtime.
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@dataclass(frozen=True)
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class DeferredToolCatalog:
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"""Immutable catalog of deferred tools. Pure search, no mutation."""
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tools: tuple[BaseTool, ...]
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@cached_property
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def names(self) -> frozenset[str]:
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return frozenset(t.name for t in self.tools)
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@cached_property
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def hash(self) -> str:
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canon = [{"name": t.name, "schema": convert_to_openai_function(t)} for t in sorted(self.tools, key=lambda t: t.name)]
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blob = json.dumps(canon, sort_keys=True, ensure_ascii=False, default=str)
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return hashlib.sha256(blob.encode("utf-8")).hexdigest()[:16]
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def search(self, query: str) -> list[BaseTool]:
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query = query.strip()
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if not query:
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return []
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if query.startswith("select:"):
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# No cap: ``select:`` names the tools explicitly, so returning a
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# subset silently drops schemas the model asked for by name. Mirrors
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# ``SkillCatalog.search`` (``skills/catalog.py``); the ranked modes
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# below stay capped at ``MAX_RESULTS``.
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wanted = {n.strip() for n in query[7:].split(",")}
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return [t for t in self.tools if t.name in wanted]
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if query.startswith("+"):
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parts = query[1:].split(None, 1)
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if not parts:
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return [] # bare "+" with no required token — nothing to require
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required = parts[0].lower()
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candidates = [t for t in self.tools if required in t.name.lower()]
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if len(parts) > 1:
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candidates.sort(key=lambda t: _catalog_regex_score(parts[1], t), reverse=True)
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return candidates[:MAX_RESULTS]
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regex = _compile_catalog_regex(query)
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scored: list[tuple[int, BaseTool]] = []
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for t in self.tools:
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searchable = f"{t.name} {t.description or ''}"
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if regex.search(searchable):
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scored.append((2 if regex.search(t.name) else 1, t))
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scored.sort(key=lambda x: x[0], reverse=True)
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return [t for _, t in scored][:MAX_RESULTS]
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def _catalog_regex_score(pattern: str, t: BaseTool) -> int:
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regex = _compile_catalog_regex(pattern)
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return len(regex.findall(f"{t.name} {t.description or ''}"))
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# ── Setup / tool ──
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@dataclass(frozen=True)
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class DeferredToolSetup:
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"""Result of assembling deferred-tool support for one agent build.
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The three fields move as a unit, so callers branch on ``tool_search_tool``:
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- **Empty** ``(None, frozenset(), None)``: deferral is disabled, or no MCP
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tool is present in the candidate list. Nothing is deferred — bind tools
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as-is.
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- **Populated**: ``tool_search_tool`` is appended to the agent's tools,
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``deferred_names`` are withheld from the model until promoted, and
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``catalog_hash`` scopes those promotions in graph state.
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Invariant: ``tool_search_tool is None`` ⟺ ``deferred_names`` is empty ⟺
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``catalog_hash is None``.
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"""
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tool_search_tool: BaseTool | None
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deferred_names: frozenset[str]
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catalog_hash: str | None
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def build_tool_search_tool(catalog: DeferredToolCatalog) -> BaseTool:
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catalog_hash = catalog.hash
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@tool
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def tool_search(query: str, tool_call_id: Annotated[str, InjectedToolCallId]) -> Command:
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"""Fetches full schema definitions for deferred tools so they can be called.
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Deferred tools appear by name in <available-deferred-tools> in the system
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prompt. Until fetched, only the name is known. This tool matches a query
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against the deferred tools and returns the matched tools complete schemas;
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once returned, a tool becomes callable.
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Query forms:
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- "select:Read,Edit" -- fetch these exact tools by name
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- "notebook jupyter" -- keyword search, up to max_results best matches
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- "+slack send" -- require "slack" in the name, rank by remaining terms
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"""
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matched = catalog.search(query)
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if not matched:
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content, names = f"No tools found matching: {query}", []
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else:
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content = json.dumps([convert_to_openai_function(t) for t in matched], indent=2, ensure_ascii=False)
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names = [t.name for t in matched]
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return Command(
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update={
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"promoted": {"catalog_hash": catalog_hash, "names": names},
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"messages": [ToolMessage(content=content, tool_call_id=tool_call_id, name="tool_search")],
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}
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)
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return tool_search
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def build_deferred_tool_setup(candidate_tools: list[BaseTool], *, enabled: bool) -> DeferredToolSetup:
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"""Build deferred-tool setup from one agent build's candidate tools.
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Lead agents pass their full configured tool list; ``SkillToolPolicyMiddleware``
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later filters model-visible schemas, execution, and ``tool_search`` results
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for the active skill while keeping the discovery tool itself available.
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Subagents may pass a statically policy-filtered list because their configured
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skills are loaded at startup. The downstream deferred-schema middleware still
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hides unpromoted MCP schemas in either case.
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Returns an empty setup (see :class:`DeferredToolSetup`) in two distinct
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cases: deferral is disabled, or it is enabled but no MCP tool survived
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the caller's build-time selection.
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"""
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if not enabled:
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# Deferral disabled: defer nothing; the model binds every tool as before.
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return DeferredToolSetup(None, frozenset(), None)
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deferred = [t for t in candidate_tools if is_mcp_tool(t)]
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if not deferred:
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# Enabled, but no MCP tool to defer: same empty result, different reason.
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return DeferredToolSetup(None, frozenset(), None)
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catalog = DeferredToolCatalog(tuple(deferred))
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return DeferredToolSetup(build_tool_search_tool(catalog), catalog.names, catalog.hash)
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def assemble_deferred_tools(candidate_tools: list[BaseTool], *, enabled: bool) -> tuple[list[BaseTool], DeferredToolSetup]:
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"""Build the final tool list and deferred setup from candidate tools.
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Fail closed on deferral assembly itself: if tool_search is enabled and MCP
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candidates exist but no deferred set was recovered, raise rather than silently
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binding their full schemas to the model. Lead-agent authorization is enforced
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separately at runtime by ``SkillToolPolicyMiddleware``; subagents may already
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have applied their static skill policy to ``candidate_tools``.
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Shared by every agent-build path (lead, embedded client, subagent) so they
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all get the same fail-closed guarantee from one place.
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"""
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deferred_setup = build_deferred_tool_setup(candidate_tools, enabled=enabled)
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if enabled and not deferred_setup.deferred_names and any(is_mcp_tool(t) for t in candidate_tools):
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raise RuntimeError("tool_search enabled and MCP candidates exist, but no deferred set was recovered - refusing to bind MCP schemas (fail-closed).")
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final_tools = list(candidate_tools)
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if deferred_setup.tool_search_tool:
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final_tools.append(deferred_setup.tool_search_tool)
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return final_tools, deferred_setup
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def _routing_priority(value: Any) -> int:
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# Produces the typed priority stored in the routing index. McpRoutingMiddleware
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# ._normalize_index re-parses this defensively (it is built to accept arbitrary
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# serialized data), so keep the two coercion rules in sync if either changes.
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try:
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return int(value)
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except (TypeError, ValueError):
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return 0
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def _routing_keywords(value: Any) -> list[str]:
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# See _routing_priority: McpRoutingMiddleware._normalize_index re-normalizes
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# keywords defensively; keep both coercion rules aligned.
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if not isinstance(value, list):
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return []
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return [keyword for keyword in (str(item).strip() for item in value) if keyword]
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def build_mcp_routing_middleware(
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tools: Iterable[BaseTool],
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deferred_setup: DeferredToolSetup,
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*,
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top_k: int,
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) -> "AgentMiddleware | None":
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"""Build PR2 auto-promotion middleware from the caller's deferred tools.
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The builder may inspect ``BaseTool.metadata`` at construction time, but the
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returned middleware receives only a flat serializable routing index.
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"""
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if deferred_setup.catalog_hash is None or not deferred_setup.deferred_names:
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return None
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routing_index: dict[str, dict[str, Any]] = {}
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for candidate in tools:
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tool_name = getattr(candidate, "name", "")
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if tool_name not in deferred_setup.deferred_names:
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continue
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routing = get_mcp_routing(candidate)
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if routing is None or routing.get("mode") != "prefer":
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continue
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keywords = _routing_keywords(routing.get("keywords"))
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if not keywords:
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continue
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if routing.get("auto_promote_top_k") is not None:
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logger.debug("Ignoring per-tool MCP routing auto_promote_top_k for %s in PR2", tool_name)
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routing_index[str(tool_name)] = {
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"priority": _routing_priority(routing.get("priority", 0)),
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"keywords": keywords,
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}
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if not routing_index:
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return None
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from deerflow.agents.middlewares.mcp_routing_middleware import McpRoutingMiddleware
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return McpRoutingMiddleware(routing_index, deferred_setup.catalog_hash, top_k)
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# Prompt rendering
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def get_deferred_tools_prompt_section(*, deferred_names: frozenset[str] = frozenset()) -> str:
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"""Generate <available-deferred-tools> from an explicit deferred-name set.
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Lists only names so the agent knows what exists and can use tool_search to
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load them. Returns empty string when there are no deferred tools. The set is
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computed at agent build time and passed in. Lead-agent sets contain the full
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configured MCP catalog because active skill policy is applied at runtime;
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subagent sets may already have been filtered by their startup skill policy.
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Lives here, next to the assembly that produces ``deferred_names``, so every
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agent-build path (lead, embedded client, subagent) renders the section the
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same way without coupling back to ``lead_agent.prompt``.
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"""
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if not deferred_names:
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return ""
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# Names come verbatim from external MCP servers; escape so a crafted tool
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# name cannot close this block and forge a framework tag. Mirrors
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# get_skill_index_prompt_section.
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names = "\n".join(html.escape(name, quote=False) for name in sorted(deferred_names))
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return f"<available-deferred-tools>\n{names}\n</available-deferred-tools>"
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def _format_keyword_list(keywords: list[str]) -> str:
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if len(keywords) == 1:
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return keywords[0]
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return f"{', '.join(keywords[:-1])}, or {keywords[-1]}"
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def get_mcp_routing_hints_prompt_section(tools: Iterable[BaseTool], *, deferred_names: frozenset[str] = frozenset()) -> str:
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"""Render <mcp_routing_hints> from MCP tools carrying routing metadata.
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When tool_search has deferred an MCP tool, the hint must point the model at
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promotion first; otherwise it may try to call a schema that is hidden from
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the bound model request.
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"""
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hints: list[tuple[int, str, list[str]]] = []
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for candidate in tools:
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routing = get_mcp_routing(candidate)
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if routing is None or routing.get("mode") != "prefer":
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continue
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keywords = routing.get("keywords") or []
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if not keywords:
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continue
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hints.append((int(routing.get("priority", 0)), candidate.name, [html.escape(str(keyword), quote=False) for keyword in keywords]))
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if not hints:
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return ""
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lines = ["<mcp_routing_hints>"]
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for priority, tool_name, keywords in sorted(hints, key=lambda item: (-item[0], item[1])):
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# tool_name comes verbatim from the external MCP server; escape at render
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# (keep the raw name for the deferred_names membership check above).
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esc_name = html.escape(tool_name, quote=False)
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lines.append(f"When the user's request involves {_format_keyword_list(keywords)}:")
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if tool_name in deferred_names:
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lines.append(f" use `tool_search` to fetch `{esc_name}`, then prefer that MCP tool.")
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else:
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lines.append(f" prefer the `{esc_name}` tool.")
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lines.append("</mcp_routing_hints>")
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return "\n".join(lines)
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