export_section_packet_v1
Generate JSON packets with evidence, paper matrices, and citations for AI-assisted academic writing and literature review automation.
Instructions
导出章节写作输入包
生成包含所有必要信息的 JSON，供 Agent 写作使用。
Args: pack_id: 证据包 ID
Returns: evidence[], paper_matrix[], claim_matrix[], doc_citations[]
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| pack_id | Yes |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Implementation Reference
- src/paperlib_mcp/tools/review.py:508-671 (handler)Full implementation of the export_section_packet_v1 tool handler. Queries database for pack contents, builds structured output with evidence chunks, paper metadata matrix, claim matrix, and citation info.
@mcp.tool() def export_section_packet_v1(pack_id: int) -> dict[str, Any]: """导出章节写作输入包 生成包含所有必要信息的 JSON，供 Agent 写作使用。 Args: pack_id: 证据包 ID Returns: evidence[], paper_matrix[], claim_matrix[], doc_citations[] """ try: # 获取 pack 信息 pack = query_one( "SELECT pack_id, query, params_json FROM evidence_packs WHERE pack_id = %s", (pack_id,), ) if not pack: return {"error": f"Pack not found: {pack_id}"} # 获取所有 chunk 内容 chunks = query_all( """ SELECT epi.doc_id, epi.chunk_id, epi.rank, c.text, c.page_start, c.page_end, d.title, d.authors, d.year FROM evidence_pack_items epi JOIN chunks c ON epi.chunk_id = c.chunk_id JOIN documents d ON epi.doc_id = d.doc_id WHERE epi.pack_id = %s ORDER BY epi.rank """, (pack_id,), ) # 构建 evidence 列表 evidence = [] doc_ids = set() chunk_ids = [] for chunk in chunks: doc_ids.add(chunk["doc_id"]) chunk_ids.append(chunk["chunk_id"]) evidence.append({ "doc_id": chunk["doc_id"], "chunk_id": chunk["chunk_id"], "page_start": chunk["page_start"], "page_end": chunk["page_end"], "text": chunk["text"], "title": chunk["title"], "authors": chunk["authors"], "year": chunk["year"], "citation_anchor": f"[[chunk:{chunk['chunk_id']}]]", }) doc_ids_list = list(doc_ids) # 构建 paper_matrix paper_matrix = [] for doc_id in doc_ids_list: doc = query_one( "SELECT doc_id, title, authors, year FROM documents WHERE doc_id = %s", (doc_id,), ) if doc: # 获取该文档关联的实体 entities = query_all( """ SELECT DISTINCT e.type, e.canonical_name FROM entities e JOIN mentions m ON e.entity_id = m.entity_id WHERE m.doc_id = %s """, (doc_id,), ) entity_by_type: dict[str, list[str]] = defaultdict(list) for ent in entities: entity_by_type[ent["type"]].append(ent["canonical_name"]) paper_matrix.append({ "doc_id": doc_id, "title": doc["title"], "authors": doc["authors"], "year": doc["year"], "topics": entity_by_type.get("Topic", []), "measures": entity_by_type.get("MeasureProxy", []), "identification_strategies": entity_by_type.get("IdentificationStrategy", []), "methods": entity_by_type.get("Method", []), "settings": entity_by_type.get("Setting", []), "limitations": entity_by_type.get("LimitationGap", []), }) # 构建 claim_matrix claim_matrix = [] if chunk_ids: claims = query_all( """ SELECT claim_id, doc_id, chunk_id, claim_text, sign, conditions, confidence FROM claims WHERE chunk_id = ANY(%s) ORDER BY confidence DESC """, (chunk_ids,), ) for claim in claims: claim_matrix.append({ "claim_id": claim["claim_id"], "doc_id": claim["doc_id"], "chunk_id": claim["chunk_id"], "claim_text": claim["claim_text"], "sign": claim["sign"], "conditions": claim["conditions"] or {}, "confidence": claim["confidence"], "citation_anchor": f"[[chunk:{claim['chunk_id']}]]", }) # 获取引用信息 doc_citations = [] for doc_id in doc_ids_list: citation = query_one( """ SELECT d.doc_id, d.title, d.authors, d.year, c.bibtex, c.apa FROM documents d LEFT JOIN citations c ON d.doc_id = c.doc_id WHERE d.doc_id = %s """, (doc_id,), ) if citation: doc_citations.append({ "doc_id": citation["doc_id"], "title": citation["title"], "authors": citation["authors"], "year": citation["year"], "bibtex": citation["bibtex"], "apa": citation["apa"], }) return { "pack_id": pack_id, "query": pack["query"], "evidence": evidence, "paper_matrix": paper_matrix, "claim_matrix": claim_matrix, "doc_citations": doc_citations, "stats": { "total_chunks": len(evidence), "unique_docs": len(doc_ids_list), "total_claims": len(claim_matrix), }, } except Exception as e: return {"error": str(e)} - src/paperlib_mcp/server.py:26-47 (registration)Imports and calls register_review_tools(mcp) to register the review tools including export_section_packet_v1 in the main MCP server.
from paperlib_mcp.tools.review import register_review_tools # M4 Canonicalization & Grouping 工具 from paperlib_mcp.tools.graph_relation_canonicalize import register_graph_relation_canonicalize_tools from paperlib_mcp.tools.graph_claim_grouping import register_graph_claim_grouping_tools from paperlib_mcp.tools.graph_v12 import register_graph_v12_tools register_health_tools(mcp) register_import_tools(mcp) register_search_tools(mcp) register_fetch_tools(mcp) register_writing_tools(mcp) # 注册 M2 GraphRAG 工具 register_graph_extract_tools(mcp) register_graph_canonicalize_tools(mcp) register_graph_community_tools(mcp) register_graph_summarize_tools(mcp) register_graph_maintenance_tools(mcp) # 注册 M3 Review 工具 register_review_tools(mcp)