Refactor context passing: pipeline now accepts dict directly

Eliminate the NamedTemporaryFile workaround in server.py by adding a
`context` dict parameter to run_freshrss_pipeline. The pipeline resolves
filter context from the dict first, falling back to context_path, then
defaulting to an empty FilterContext. Remove unused json/tempfile imports.

src/summary_mcp/server.py

@@ -3,8 +3,6 @@ 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
 from pathlib import Path
 
@@ -95,38 +93,26 @@ 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:
-        if context is not None:
-            with tempfile.NamedTemporaryFile("w", encoding="utf-8", suffix=".json", delete=False) as handle:
-                json.dump(context, handle, ensure_ascii=False, indent=2)
-                temp_context_path = Path(handle.name)
-
-        result = run_freshrss_pipeline(
-            api_base_url=api_base_url,
-            username=username,
-            api_password=api_password,
-            stream_id=stream_id,
-            limit=limit,
-            continuation=continuation,
-            include_read=include_read,
-            mark_read=mark_read,
-            debug_artifacts=debug_artifacts,
-            context_path=temp_context_path,
-            max_retries=max_retries,
-            timeout_seconds=timeout_seconds,
-            llm_api_key=llm_api_key,
-            llm_model=llm_model,
-            llm_api_url=llm_api_url,
-            run_id=run_id,
-            delivery_date=date.fromisoformat(date_value) if date_value else None,
-            output_dir=Path(output_dir) if output_dir else None,
-        )
-    finally:
-        if temp_context_path and temp_context_path.exists():
-            temp_context_path.unlink()
+    result = run_freshrss_pipeline(
+        api_base_url=api_base_url,
+        username=username,
+        api_password=api_password,
+        stream_id=stream_id,
+        limit=limit,
+        continuation=continuation,
+        include_read=include_read,
+        mark_read=mark_read,
+        debug_artifacts=debug_artifacts,
+        context=context,
+        max_retries=max_retries,
+        timeout_seconds=timeout_seconds,
+        llm_api_key=llm_api_key,
+        llm_model=llm_model,
+        llm_api_url=llm_api_url,
+        run_id=run_id,
+        delivery_date=date.fromisoformat(date_value) if date_value else None,
+        output_dir=Path(output_dir) if output_dir else None,
+    )
 
     if not include_item_reports:
         result = {key: value for key, value in result.items() if key != "items"}

src/summary_mcp/workflows/freshrss_pipeline.py

@@ -80,6 +80,7 @@ def run_freshrss_pipeline(
     debug_artifacts: bool = False,
     prompt: Path | None = None,
     rules: Path | None = None,
+    context: dict[str, Any] | None = None,
     context_path: Path | None = None,
     max_retries: int = 2,
     timeout_seconds: float = 60.0,
@@ -137,7 +138,13 @@ def run_freshrss_pipeline(
         _save_json(items_list_output, [item.model_dump(mode="json") for item in items])
 
     loaded_rules = load_filter_rules(resolved_rules_path)
-    context = FilterContext.model_validate(_load_json(context_path)) if context_path else FilterContext()
+    # context 优先使用直接传入的 dict，其次读取 context_path 文件，两者均缺失则使用空 context
+    if context is not None:
+        filter_context = FilterContext.model_validate(context)
+    elif context_path is not None:
+        filter_context = FilterContext.model_validate(_load_json(context_path))
+    else:
+        filter_context = FilterContext()
 
     delivered_candidates: list[OpenClawCandidateInput] = []
     delivered_item_ids: list[str] = []
@@ -204,7 +211,7 @@ def run_freshrss_pipeline(
 
         summary = LlmSummaryResult.model_validate(summary_payload)
         decision = evaluate_filter_rules(
-            FilterInput(item=item, article=extraction.article, summary=summary, context=context),
+            FilterInput(item=item, article=extraction.article, summary=summary, context=filter_context),
             loaded_rules,
         )
         if filter_path is not None: