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91f6a1c242
- paper_writer_service(8097)：FastAPI + MCP streamable-http 单端点，9 工具
（propose_paper_outline/write_paper_section/get_paper_progress/search_refs/
revise_paper_section/revise_paper/get_paper/complete_paper/cancel_paper），
SQLite 三表（papers/paper_sections/citations，WAL），GB/T 7714 引用协议
- BFF：services/paper/{mcp_client,agent}.py（裸 JSON-RPC 客户端 + 规则状态机）、
routers/paper.py、schemas.py 4 个 Paper*Block + ArtifactBlock 加 paper_id/citation_count、
config.py 开关、main.py 条件注册、docgen.py 抽 insert_artifact 公共 helper
- 前端：intent.js paperIntent 纯函数、Composer docgen 分支 sub-route、sendPaper/轮询、
PaperBriefCard/PaperOutlineCard/PaperProgressCard、DocPane cite-chip
- deploy/paper_restart.sh + toggle_paper.sh；compile_sdk.sh 白名单追加 paper 文件
- tests/paper 26 条（工具逻辑 + MCP 协议 + BFF 状态机 + 块 schema + 前端 intent 单测）
70 lines
2.6 KiB
Python
70 lines
2.6 KiB
Python
"""tests/paper/test_mcp_protocol.py —— MCP 协议层测试（list_tools / call_tool 往返）。
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验证：9 个工具正确暴露、call_tool 返回可解析的 JSON（structuredContent 或 content 文本块）。
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镜像 bioflow_service/tests/test_mcp_server.py 的 call_tool 集成模式。
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"""
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from __future__ import annotations
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import json
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from conftest import _run
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from paper_writer_service.app.mcp import paper_mcp
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EXPECTED_TOOLS = {
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"propose_paper_outline", "write_paper_section", "get_paper_progress", "search_refs",
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"revise_paper_section", "revise_paper", "get_paper", "complete_paper", "cancel_paper",
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}
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def _content_text(result):
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"""从 CallToolResult 抽取文本内容（无 structuredContent 时）。"""
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for block in getattr(result, "content", None) or []:
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text = getattr(block, "text", None)
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if text:
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return json.loads(text)
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return {}
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def _call(name, args):
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r = _run(paper_mcp.call_tool(name, args))
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return getattr(r, "structuredContent", None) or _content_text(r)
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def test_list_tools_exposes_nine():
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tools = _run(paper_mcp.list_tools())
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names = {getattr(t, "name", None) for t in tools}
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assert EXPECTED_TOOLS == names
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def test_call_tool_propose_outline_roundtrip(paper_db, fake_llm, fake_search):
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fake_llm.responses.append({
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"title": "水稻SPL基因家族研究进展",
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"abstract_expect": "综述",
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"sections": [{"section_id": "s1", "title": "引言", "key_points": [], "target_words": 1500}],
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})
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r = _call("propose_paper_outline", {"topic": "水稻SPL基因家族", "paper_type": "综述"})
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assert r.get("ok") is True
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assert r["paper_id"]
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assert r["title"] == "水稻SPL基因家族研究进展"
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def test_call_tool_not_found(paper_db):
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r = _call("get_paper", {"paper_id": "nope"})
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assert r.get("ok") is False
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assert r.get("code") == "NOT_FOUND"
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def test_call_tool_content_is_json_string(paper_db, fake_llm, fake_search):
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"""验证 content[0].text 是合法 JSON 字符串（BFF 裸 JSON-RPC 客户端解析面）。"""
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fake_llm.responses.append({
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"title": "T", "abstract_expect": "A",
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"sections": [{"section_id": "s1", "title": "引言", "key_points": [], "target_words": 1500}],
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})
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raw = _run(paper_mcp.call_tool("propose_paper_outline", {"topic": "水稻SPL"}))
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blocks = getattr(raw, "content", None) or []
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assert blocks, "CallToolResult 无 content 块"
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text = getattr(blocks[0], "text", None)
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assert text is not None
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payload = json.loads(text) # 应为合法 JSON 字符串
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assert payload.get("ok") is True
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