Add inline comments to core modules and code review notes

Added explanatory comments to server.py, freshrss_pipeline.py, pipeline.py,
freshrss.py, keyword_index.py, summary_loop.py, and filters/engine.py.
Also added CODE_REVIEW.md with prioritized improvement suggestions.

src/summary_mcp/core/keyword_index.py

@@ -1,5 +1,8 @@
 from __future__ import annotations
 
+# 日报级关键词索引：从当日 delivery 候选中提取关键词，
+# 应用别名归并和停用词过滤，输出 DailyKeywordIndex 并聚合全局 KeywordStatsIndex。
+
 import json
 from collections import Counter
 from datetime import UTC, date, datetime
@@ -83,6 +86,7 @@ def normalize_keyword(
     aliases: dict[str, str],
     stopwords: set[str],
 ) -> str | None:
+    # 对单个关键词做别名替换 + 停用词过滤，返回 None 表示丢弃
     raw_value = keyword.strip()
     if not raw_value:
         return None
@@ -200,6 +204,7 @@ def persist_keyword_indexes(
     stopwords_path: Path | None = None,
     include_decisions: set[str] | None = None,
 ) -> dict[str, object]:
+    # 构建并写入当日词元索引，同时全量重建全局词频统计；每次 pipeline 运行后自动调用
     aliases = load_term_aliases(aliases_path)
     stopwords = load_term_stopwords(stopwords_path)
     daily_index = build_daily_keyword_index(

src/summary_mcp/core/pipeline.py

@@ -1,5 +1,8 @@
 from __future__ import annotations
 
+# 内容提取主流程：根据 item 来源决定使用内联 RSS 内容还是回源抓取，
+# 再经过文本提取、质量评估、文章构建，输出标准化 ExtractionOutput。
+
 from summary_mcp.core.content_loader import choose_inline_content, fetch_html
 from summary_mcp.core.errors import SummaryError
 from summary_mcp.core.extractor import extract_plain_text, extract_title
@@ -9,10 +12,12 @@ from summary_mcp.core.quality_checker import assess_quality
 from summary_mcp.models.summary_io import DebugInfo, ExtractionInput, ExtractionOutput
 
 
+# FreshRSS 来源的 item 不回源抓网页，直接使用 RSS 内联内容
 RSS_ONLY_UPSTREAMS = {"freshrss"}
 
 
 def _should_skip_fetch(extraction_input: ExtractionInput) -> bool:
+    # 判断当前 item 是否属于 RSS-only 上游，若是则禁止回源抓取
     item = extraction_input.item
     if item is None:
         return False

src/summary_mcp/core/summary_loop.py

@@ -1,5 +1,8 @@
 from __future__ import annotations
 
+# LLM 摘要循环：构造提示词 -> 调用 LLM -> 解析 JSON -> 校验 -> 失败时生成修复提示词重试。
+# 核心入口：run_loop_payload()，最多重试 max_retries 次。
+
 import json
 import os
 import re

src/summary_mcp/filters/engine.py

@@ -1,5 +1,8 @@
 from __future__ import annotations
 
+# 确定性规则引擎：从 JSON 配置加载规则，按优先级评估每条规则的条件，
+# 输出 keep / review / drop 决策及命中规则列表。
+
 import json
 from pathlib import Path
 from typing import Any
@@ -32,6 +35,7 @@ def _normalize_value(value: Any) -> Any:
 
 
 def _resolve_field(filter_input: FilterInput, field_path: str) -> Any:
+    # 按点分路径从 FilterInput 中取值，路径不存在时返回 None
     current: Any = filter_input
     for part in field_path.split("."):
         current = _normalize_value(current)
@@ -94,6 +98,8 @@ def _rule_matches(filter_input: FilterInput, rule: FilterRule) -> bool:
 
 
 def evaluate_filter_rules(filter_input: FilterInput, rules: list[FilterRule]) -> FilterDecisionResult:
+    # 按优先级排序后逐条评估规则；stop_on_match=True 时命中即终止
+    # 最终决策优先级：drop > keep > review；无规则命中时默认 review
     matched: list[MatchedRule] = []
 
     for rule in sorted(rules, key=lambda item: item.action.priority, reverse=True):

src/summary_mcp/integrations/freshrss.py

@@ -1,5 +1,8 @@
 from __future__ import annotations
 
+# FreshRSS GReader API 集成：负责登录鉴权、拉取未读条目、将 entry 映射为标准 item、
+# 以及成功投递后将条目标记为已读。
+
 import hashlib
 from datetime import UTC, datetime
 from typing import Any
@@ -12,6 +15,7 @@ from summary_mcp.models.item import Item
 
 READ_TAG = "user/-/state/com.google/read"
 KEPT_UNREAD_TAG = "user/-/state/com.google/kept-unread"
+# RSS 内联内容低于此长度时视为无效，item 将被标记为 pending 并跳过
 MIN_INLINE_CONTENT_LENGTH = 500
 
 
@@ -124,6 +128,8 @@ def _has_usable_inline_content(value: str | None) -> bool:
 
 
 def map_entry_to_item(entry: dict[str, Any]) -> Item:
+    # 将 FreshRSS GReader API 返回的原始 entry dict 映射为标准化 Item 对象
+    # fetch_state: 若内联内容足够长则为 'fetched'，否则为 'pending'
     url = _pick_entry_url(entry)
     summary_html = _pick_content_block(entry, "summary")
     content_html = _pick_content_block(entry, "content")

src/summary_mcp/server.py

@@ -1,5 +1,8 @@
 from __future__ import annotations
 
+# MCP 服务入口：将内容提取、过滤、FreshRSS 全链路管道暴露为 MCP 工具。
+# 生产主入口是 run_freshrss_openclaw_pipeline，其余工具供单步调试使用。
+
 import json
 import tempfile
 from datetime import date
@@ -22,6 +25,7 @@ mcp = FastMCP(name="content-extract-mcp")
 
 @mcp.tool()
 def extract_url_content(url: str, language_hint: str | None = None) -> dict:
+    # 从单个 URL 抓取并提取结构化文章内容，供单步调试使用
     """Extract structured article content from a single URL."""
     result = extract_content(
         ExtractionInput(
@@ -34,6 +38,7 @@ def extract_url_content(url: str, language_hint: str | None = None) -> dict:
 
 @mcp.tool()
 def extract_item_content(item: dict) -> dict:
+    # 从已标准化的 item 对象提取内容（跳过网络抓取，使用 RSS 内联内容）
     """Extract structured article content from a normalized item object."""
     parsed_item = Item.model_validate(item)
     result = extract_content(ExtractionInput(item=parsed_item))
@@ -47,6 +52,7 @@ def filter_summary_result(
     item: dict | None = None,
     context: dict | None = None,
 ) -> dict:
+    # 对结构化摘要结果运行确定性规则引擎，返回 keep/review/drop 决策
     """Apply deterministic filter rules to a structured summary result."""
     parsed_summary = LlmSummaryResult.model_validate(summary_result)
     parsed_article = ExtractedArticle.model_validate(extracted_article) if extracted_article else None
@@ -88,6 +94,7 @@ def run_freshrss_openclaw_pipeline(
     include_item_reports: bool = False,
 ) -> dict:
     """Run the full FreshRSS -> extract -> LLM -> filter -> OpenClaw payload pipeline."""
+    # context 若传入则序列化为临时文件再传给 pipeline（TODO: pipeline 改为直接接受 dict）
     temp_context_path: Path | None = None
 
     try:

src/summary_mcp/workflows/freshrss_pipeline.py

@@ -1,5 +1,9 @@
 from __future__ import annotations
 
+# FreshRSS 全链路管道：拉取未读条目 -> 内容提取 -> LLM 摘要 -> 规则过滤 ->
+# 构建 OpenClaw delivery payload -> 写盘 -> 词元统计 -> 标记已读。
+# 生产入口：run_freshrss_pipeline()，由 MCP 工具 run_freshrss_openclaw_pipeline 调用。
+
 import json
 import os
 from datetime import UTC, date, datetime
@@ -46,6 +50,7 @@ def _load_json(path: Path) -> dict[str, Any]:
 
 
 def _load_required_env(name: str, value: str | None) -> str:
+    # 优先使用传入的 value，否则读取同名环境变量；两者均缺失时抛出 RuntimeError
     resolved = value or os.environ.get(name)
     if not resolved:
         raise RuntimeError(f"Missing required value: {name}.")
@@ -137,6 +142,7 @@ def run_freshrss_pipeline(
     item_reports: list[dict[str, Any]] = []
 
     for index, item in enumerate(items, start=1):
+        # 逐条处理：提取 -> 摘要 -> 过滤 -> 候选构建；任一步骤失败则记录状态后 continue
         item_key = f"item-{index:02d}"
         item_path = _maybe_path(debug_artifacts, resolved_output_dir / "items" / f"{item_key}.item.json")
         extracted_path = _maybe_path(debug_artifacts, resolved_output_dir / "extracted" / f"{item_key}.extracted.json")
@@ -256,6 +262,7 @@ def run_freshrss_pipeline(
 
     marked_count = 0
     if mark_read and delivered_item_ids:
+        # 仅标记成功投递（delivered）的 item；drop/review 的 item 保持未读状态
         client.mark_items_as_read(auth_token=auth_token, item_ids=delivered_item_ids)
         marked_count = len({item_id for item_id in delivered_item_ids if item_id})