feat: Implement Literature MCP for multi-source academic literature search

.gitignore

@@ -11,6 +11,7 @@ Thumbs.db
 *.bak
 *.swp
 **/tmp/
+.vs/
 
 # ===== 系统文件 =====
 # Windows
@@ -160,3 +161,6 @@ docker-compose.override.yml
 .github/copilot-instructions.md
 CLAUDE.md
 GEMINI.md
+
+# MCP说明文档
+/service/docs/literature

service/app/mcp/literature.py

@@ -0,0 +1,376 @@
+"""
+Literature MCP Server - Multi-source academic literature search
+
+Provides tools for searching academic literature from multiple data sources
+(OpenAlex, Semantic Scholar, PubMed, etc.) with unified interface.
+"""
+
+import json
+import logging
+from datetime import datetime
+from typing import Any
+
+import httpx
+from fastmcp import FastMCP
+
+from app.utils.literature import SearchRequest, WorkDistributor
+
+logger = logging.getLogger(__name__)
+
+# Create FastMCP instance
+mcp = FastMCP("literature")
+
+# Metadata for MCP server
+__mcp_metadata__ = {
+    "name": "Literature Search",
+    "description": "Search academic literature from multiple sources with advanced filtering",
+    "version": "1.0.0",
+}
+
+
+@mcp.tool()
+async def search_literature(
+    query: str,
+    mailto: str | None = None,
+    author: str | None = None,
+    institution: str | None = None,
+    source: str | None = None,
+    year_from: str | None = None,
+    year_to: str | None = None,
+    is_oa: str | None = None,
+    work_type: str | None = None,
+    language: str | None = None,
+    is_retracted: str | None = None,
+    has_abstract: str | None = None,
+    has_fulltext: str | None = None,
+    sort_by: str = "relevance",
+    max_results: str | int = 50,
+    data_sources: list[str] | None = None,
+    include_abstract: str | bool = False,
+) -> str:
+    """
+    Search academic literature from multiple data sources (OpenAlex, etc.)
+
+    ⚠️ IMPORTANT: A valid email address (mailto parameter) enables the OpenAlex polite pool
+    (10 req/s). If omitted, the default pool is used (1 req/s, sequential). Production
+    usage should provide an email.
+
+    Basic usage: Provide query keywords and user's email. Returns a Markdown report
+    with statistics and JSON list of papers.
+
+    Args:
+        query: Search keywords (e.g., "machine learning", "CRISPR")
+        mailto: OPTIONAL - User's email (e.g., "researcher@university.edu")
+        author: OPTIONAL - Author name (e.g., "Albert Einstein")
+        institution: OPTIONAL - Institution (e.g., "MIT", "Harvard University")
+        source: OPTIONAL - Journal (e.g., "Nature", "Science")
+        year_from: OPTIONAL - Start year (e.g., "2020" or 2020)
+        year_to: OPTIONAL - End year (e.g., "2024" or 2024)
+        is_oa: OPTIONAL - Open access only ("true"/"false")
+        work_type: OPTIONAL - Work type: "article", "review", "preprint", "book", "dissertation", etc.
+        language: OPTIONAL - Language code (e.g., "en" for English, "zh" for Chinese, "fr" for French)
+        is_retracted: OPTIONAL - Filter retracted works ("true" to include only retracted, "false" to exclude)
+        has_abstract: OPTIONAL - Require abstract ("true" to include only works with abstracts)
+        has_fulltext: OPTIONAL - Require full text ("true" to include only works with full text)
+        sort_by: Sort: "relevance" (default), "cited_by_count", "publication_date"
+        max_results: Max papers (default: 50, range: 1-200, accepts string or int)
+        data_sources: Sources to search (default: ["openalex"])
+        include_abstract: Include abstracts (default: False, accepts string or bool)
+
+    Returns:
+        Markdown report with:
+        - Warnings if filters fail
+        - Statistics (citations, open access rate)
+        - JSON list of papers (title, authors, DOI, etc.)
+        - Next steps guidance
+
+    Usage tips:
+        - START SIMPLE: just query + mailto
+        - Tool will suggest corrections if author/institution not found
+        - Review "Next Steps Guide" before searching again
+
+    Examples:
+        # Minimal (recommended)
+        search_literature("machine learning", mailto="researcher@uni.edu")
+
+        # With filters (accepts both strings and integers)
+        search_literature(
+            query="CRISPR",
+            mailto="researcher@uni.edu",
+            author="Jennifer Doudna",
+            year_from="2020",
+            year_to="2024"
+        )
+
+        # Recent reviews (past 5 years, English only)
+        search_literature(
+            query="cancer immunotherapy",
+            mailto="user@example.com",
+            work_type="review",
+            language="en",
+            year_from="2020",
+            sort_by="cited_by_count"
+        )
+
+        # Research articles with abstracts (exclude retracted)
+        search_literature(
+            query="CRISPR gene editing",
+            mailto="user@example.com",
+            work_type="article",
+            has_abstract="true",
+            is_retracted="false"
+        )
+    """
+    try:
+        # Convert string parameters to proper types
+        year_from_int = int(year_from) if year_from and str(year_from).strip() else None
+        year_to_int = int(year_to) if year_to and str(year_to).strip() else None
+
+        # Clamp year ranges (warn but don't block search)
+        max_year = datetime.now().year + 1
+        year_warning = ""
+        if year_from_int is not None and year_from_int > max_year:
+            year_warning += f"year_from {year_from_int}→{max_year}. "
+            year_from_int = max_year
+        if year_to_int is not None and year_to_int < 1700:
+            year_warning += f"year_to {year_to_int}→1700. "
+            year_to_int = 1700
+
+        # Convert is_oa to boolean
+        is_oa_bool: bool | None = None
+        if is_oa is not None:
+            is_oa_bool = str(is_oa).lower() in ("true", "1", "yes")
+
+        # Convert is_retracted to boolean
+        is_retracted_bool: bool | None = None
+        if is_retracted is not None:
+            is_retracted_bool = str(is_retracted).lower() in ("true", "1", "yes")
+
+        # Convert has_abstract to boolean
+        has_abstract_bool: bool | None = None
+        if has_abstract is not None:
+            has_abstract_bool = str(has_abstract).lower() in ("true", "1", "yes")
+
+        # Convert has_fulltext to boolean
+        has_fulltext_bool: bool | None = None
+        if has_fulltext is not None:
+            has_fulltext_bool = str(has_fulltext).lower() in ("true", "1", "yes")
+
+        # Convert max_results to int
+        max_results_int = int(max_results) if max_results else 50
+
+        # Convert include_abstract to bool
+        include_abstract_bool = str(include_abstract).lower() in ("true", "1", "yes") if include_abstract else False
+
+        openalex_email = mailto.strip() if mailto and str(mailto).strip() else None
+
+        logger.info(
+            f"Literature search requested: query='{query}', mailto={openalex_email}, max_results={max_results_int}"
+        )
+
+        # Create search request with converted types
+        request = SearchRequest(
+            query=query,
+            author=author,
+            institution=institution,
+            source=source,
+            year_from=year_from_int,
+            year_to=year_to_int,
+            is_oa=is_oa_bool,
+            work_type=work_type,
+            language=language,
+            is_retracted=is_retracted_bool,
+            has_abstract=has_abstract_bool,
+            has_fulltext=has_fulltext_bool,
+            sort_by=sort_by,
+            max_results=max_results_int,
+            data_sources=data_sources,
+        )
+
+        # Execute search
+        async with WorkDistributor(openalex_email=openalex_email) as distributor:
+            result = await distributor.search(request)
+
+        if year_warning:
+            result.setdefault("warnings", []).append(f"⚠️ Year adjusted: {year_warning.strip()}")
+
+        # Format output
+        return _format_search_result(request, result, include_abstract_bool)
+
+    except ValueError as e:
+        logger.warning(f"Literature search validation error: {e}")
+        return f"❌ Invalid input: {str(e)}"
+    except httpx.HTTPError as e:
+        logger.error(f"Literature search network error: {e}", exc_info=True)
+        return "❌ Network error while contacting literature sources. Please try again later."
+    except Exception as e:
+        logger.error(f"Literature search failed: {e}", exc_info=True)
+        return "❌ Unexpected error during search. Please retry or contact support."
+
+
+def _format_search_result(request: SearchRequest, result: dict[str, Any], include_abstract: bool = False) -> str:
+    """
+    Format search results into human-readable report + JSON data
+
+    Args:
+        request: Original search request
+        result: Search result from WorkDistributor
+        include_abstract: Whether to include abstracts in JSON (default: False to save tokens)
+
+    Returns:
+        Formatted markdown report with embedded JSON
+    """
+    works = result["works"]
+    total_count = result["total_count"]
+    unique_count = result["unique_count"]
+    sources = result["sources"]
+    warnings = result.get("warnings", [])
+
+    # Build report sections
+    sections: list[str] = []
+
+    # Header
+    sections.append("# Literature Search Report\n")
+
+    # Warnings and resolution status (if any)
+    if warnings:
+        sections.append("## ⚠️ Warnings and Resolution Status\n")
+        for warning in warnings:
+            sections.append(f"{warning}")
+        sections.append("")
+
+    # Search conditions
+    sections.append("## Search Conditions\n")
+    conditions: list[str] = []
+    conditions.append(f"- **Query**: {request.query}")
+    if request.author:
+        conditions.append(f"- **Author**: {request.author}")
+    if request.institution:
+        conditions.append(f"- **Institution**: {request.institution}")
+    if request.source:
+        conditions.append(f"- **Source**: {request.source}")
+    if request.year_from or request.year_to:
+        year_range = f"{request.year_from or '...'} - {request.year_to or '...'}"
+        conditions.append(f"- **Year Range**: {year_range}")
+    if request.is_oa is not None:
+        conditions.append(f"- **Open Access Only**: {'Yes' if request.is_oa else 'No'}")
+    if request.work_type:
+        conditions.append(f"- **Work Type**: {request.work_type}")
+    if request.language:
+        conditions.append(f"- **Language**: {request.language}")
+    if request.is_retracted is not None:
+        conditions.append(f"- **Exclude Retracted**: {'No' if request.is_retracted else 'Yes'}")
+    if request.has_abstract is not None:
+        conditions.append(f"- **Require Abstract**: {'Yes' if request.has_abstract else 'No'}")
+    if request.has_fulltext is not None:
+        conditions.append(f"- **Require Full Text**: {'Yes' if request.has_fulltext else 'No'}")
+    conditions.append(f"- **Sort By**: {request.sort_by}")
+    conditions.append(f"- **Max Results**: {request.max_results}")
+    sections.append("\n".join(conditions))
+    sections.append("")
+
+    # Check if no results
+    if not works:
+        sections.append("## ❌ No Results Found\n")
+        sections.append("**Suggestions to improve your search:**\n")
+        suggestions: list[str] = []
+        suggestions.append("1. **Simplify keywords**: Try broader or different terms")
+        if request.author:
+            suggestions.append("2. **Remove author filter**: Author name may not be recognized")
+        if request.institution:
+            suggestions.append("3. **Remove institution filter**: Try without institution constraint")
+        if request.source:
+            suggestions.append("4. **Remove source filter**: Try without journal constraint")
+        if request.year_from or request.year_to:
+            suggestions.append("5. **Expand year range**: Current range may be too narrow")
+        if request.is_oa:
+            suggestions.append("6. **Remove open access filter**: Include non-OA papers")
+        suggestions.append("7. **Check spelling**: Verify all terms are spelled correctly")
+        sections.append("\n".join(suggestions))
+        sections.append("")
+        return "\n".join(sections)
+
+    # Statistics and overall insights
+    sections.append("## Search Statistics\n")
+    stats: list[str] = []
+    stats.append(f"- **Total Found**: {total_count} works")
+    stats.append(f"- **After Deduplication**: {unique_count} works")
+    source_info = ", ".join(f"{name}: {count}" for name, count in sources.items())
+    stats.append(f"- **Data Sources**: {source_info}")
+
+    # Add insights
+    if works:
+        avg_citations = sum(w.cited_by_count for w in works) / len(works)
+        stats.append(f"- **Average Citations**: {avg_citations:.1f}")
+
+        oa_count = sum(1 for w in works if w.is_oa)
+        oa_ratio = (oa_count / len(works)) * 100
+        stats.append(f"- **Open Access Rate**: {oa_ratio:.1f}% ({oa_count}/{len(works)})")
+
+        years = [w.publication_year for w in works if w.publication_year]
+        if years:
+            stats.append(f"- **Year Range**: {min(years)} - {max(years)}")
+
+    sections.append("\n".join(stats))
+    sections.append("")
+
+    # Complete JSON list
+    sections.append("## Complete Works List (JSON)\n")
+    if include_abstract:
+        sections.append("The following JSON contains all works with full abstracts:\n")
+    else:
+        sections.append("The following JSON contains all works (abstracts excluded to save tokens):\n")
+    sections.append("```json")
+
+    # Convert works to dict for JSON serialization
+    works_dict = []
+    for work in works:
+        work_data = {
+            "id": work.id,
+            "doi": work.doi,
+            "title": work.title,
+            "authors": work.authors[:5],  # Limit to first 5 authors
+            "publication_year": work.publication_year,
+            "cited_by_count": work.cited_by_count,
+            "journal": work.journal,
+            "is_oa": work.is_oa,
+            "oa_url": work.oa_url,
+            "source": work.source,
+        }
+        # Only include abstract if requested
+        if include_abstract and work.abstract:
+            work_data["abstract"] = work.abstract
+        works_dict.append(work_data)
+
+    sections.append(json.dumps(works_dict, indent=2, ensure_ascii=False))
+    sections.append("```")
+    sections.append("")
+
+    # Next steps guidance - prevent infinite loops
+    sections.append("---")
+    sections.append("## 🎯 Next Steps Guide\n")
+    sections.append("**Before making another search, consider:**\n")
+    next_steps: list[str] = []
+
+    if unique_count > 0:
+        next_steps.append("✓ **Results found** - Review the JSON data above for your analysis")
+        if unique_count >= request.max_results:
+            next_steps.append(
+                f"⚠️ **Result limit reached** ({request.max_results}) - "
+                "Consider narrowing filters (author, year, journal) for more targeted results"
+            )
+        if unique_count < 10:
+            next_steps.append("💡 **Few results** - Consider broadening your search by removing some filters")
+
+    next_steps.append("")
+    next_steps.append("**To refine your search:**")
+    next_steps.append("- If too many results → Add more specific filters (author, institution, journal, year)")
+    next_steps.append("- If too few results → Remove filters or use broader keywords")
+    next_steps.append("- If wrong results → Check filter spelling and try variations")
+    next_steps.append("")
+    next_steps.append("⚠️ **Important**: Avoid making multiple similar searches without reviewing results first!")
+    next_steps.append("Each search consumes API quota and context window. Make targeted, deliberate queries.")
+
+    sections.append("\n".join(next_steps))
+
+    return "\n".join(sections)