collect_evidence
Search academic literature for evidence on a specific topic, optionally focusing on particular sections like methodology or findings, to support research and analysis.
Instructions
收集特定主题的文献证据
搜索与主题相关的文献片段，可选择聚焦于特定章节类型。
Args: topic: 搜索主题 section_focus: 聚焦的章节类型（如 "methodology", "findings"） k: 返回结果数量
Returns: 按文献聚合的证据列表
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | ||
| section_focus | No | ||
| k | No |
Implementation Reference
- The core handler implementation for the 'collect_evidence' tool. It adjusts the query based on optional section_focus, performs a hybrid search using hybrid_search, aggregates evidence chunks by document with metadata, sorts by evidence count per doc, and returns a structured dictionary.@mcp.tool() async def collect_evidence( topic: str, section_focus: str | None = None, k: int = 20, ) -> dict[str, Any]: """收集特定主题的文献证据 搜索与主题相关的文献片段，可选择聚焦于特定章节类型。 Args: topic: 搜索主题 section_focus: 聚焦的章节类型（如 "methodology", "findings"） k: 返回结果数量 Returns: 按文献聚合的证据列表 """ try: # 如果有章节聚焦，调整查询 query = topic if section_focus: focus_keywords = { "methodology": "method approach model estimation identification", "findings": "result finding evidence show demonstrate", "theory": "theory framework hypothesis prediction", "data": "data sample variable measure", } if section_focus in focus_keywords: query = f"{topic} {focus_keywords[section_focus]}" # 搜索 search_result = await hybrid_search(query, k=k, alpha=0.6, per_doc_limit=5) # 按文档聚合 evidence_by_doc: dict[str, dict] = {} for result in search_result.results: doc_id = result.doc_id if doc_id not in evidence_by_doc: # 获取文档信息 doc = query_one( "SELECT title, authors, year FROM documents WHERE doc_id = %s", (doc_id,) ) evidence_by_doc[doc_id] = { "doc_id": doc_id, "title": doc["title"] if doc else "Unknown", "authors": doc["authors"] if doc else "Unknown", "year": doc["year"] if doc else None, "evidence": [], } evidence_by_doc[doc_id]["evidence"].append({ "chunk_id": result.chunk_id, "page_start": result.page_start, "page_end": result.page_end, "text": result.snippet, "relevance_score": result.score_total, }) # 按证据数量排序 sorted_evidence = sorted( evidence_by_doc.values(), key=lambda x: len(x["evidence"]), reverse=True ) return { "topic": topic, "section_focus": section_focus, "total_chunks": len(search_result.results), "unique_documents": len(sorted_evidence), "evidence": sorted_evidence, } except Exception as e: return { "error": str(e), "topic": topic, "evidence": [], }
- src/paperlib_mcp/server.py:37-37 (registration)Registers the writing tools module (including collect_evidence) by calling register_writing_tools on the FastMCP instance in the main server file.register_writing_tools(mcp)
- src/paperlib_mcp/tools/writing.py:802-802 (registration)The @mcp.tool() decorator directly registers the collect_evidence function as an MCP tool within the register_writing_tools function.@mcp.tool()