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    "title": "DBLP Computer Science Publication Search",
    "description": "Search and extract computer science publications from DBLP -- the largest open bibliography database for computer science with over 6 million publications from journals, conferences, and workshops. Filter by keyword, author, venue, year, and publication type.",
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            "enum": [
              "auto",
              "standard",
              "literatureReview",
              "monitor",
              "citationGraph",
              "authorAnalysis"
            ],
            "type": "string",
            "description": "Pre-configured workflow. **Auto** picks the best mode for your input shape (DOI → citationGraph; author-only → authorAnalysis; diffWithPriorRun → monitor; short query + maxResults ≥ 30 → literatureReview; otherwise standard). Or pick a mode explicitly. Explicit input fields always override mode defaults.",
            "default": "auto"
          },
          "persona": {
            "title": "Persona (scoring weights)",
            "enum": [
              "researcher",
              "engineer",
              "analyst"
            ],
            "type": "string",
            "description": "Tunes the score weights to your job. Researcher: depth + cross-confirmation matter most (default). Engineer: recent papers weighted higher even at lower citations. Analyst: citation impact dominates, recency is secondary.",
            "default": "researcher"
          },
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            "title": "Search Query",
            "type": "string",
            "description": "Free-text keywords against titles, abstracts, and metadata. Leave blank if you only want to filter by author, venue, year, or DOI.",
            "default": "deep learning"
          },
          "author": {
            "title": "Author Name",
            "type": "string",
            "description": "Filter by author name (e.g. \"Yoshua Bengio\"). Required for Author intelligence mode."
          },
          "venue": {
            "title": "Venue / Conference / Journal",
            "type": "string",
            "description": "Filter by venue (e.g. \"NeurIPS\", \"Nature\", \"VLDB\")."
          },
          "year": {
            "title": "Publication Year",
            "type": "string",
            "description": "Single year (e.g. \"2024\"). Year ranges aren't supported across all sources."
          },
          "type": {
            "title": "Publication Type",
            "enum": [
              "",
              "Conference and Workshop Papers",
              "Journal Articles",
              "Informal and Other Publications",
              "Parts in Books or Collections"
            ],
            "type": "string",
            "description": "Restrict to one type. \"All Types\" combines everything.",
            "default": ""
          },
          "doi": {
            "title": "DOI Lookup (overrides search)",
            "type": "string",
            "description": "Look up a single publication by DOI (e.g. \"10.1038/nature14539\"). When set, search inputs above are ignored and the actor returns one enriched record. DBLP is skipped automatically — DOI lookup uses OpenAlex / Semantic Scholar / Crossref. Required for Citation graph mode."
          },
          "sources": {
            "title": "Sources to Query",
            "uniqueItems": true,
            "type": "array",
            "description": "Which catalogs to search. Default queries all four in parallel and merges duplicates by DOI. The actor automatically drops DBLP for biology/medical queries since DBLP indexes computer science only.",
            "items": {
              "type": "string",
              "enum": [
                "dblp",
                "openalex",
                "semanticscholar",
                "crossref"
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                "DBLP (computer science)",
                "OpenAlex (250M+ works)",
                "Semantic Scholar (200M+ papers, citations)",
                "Crossref (DOI metadata)"
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            },
            "default": [
              "dblp",
              "openalex",
              "semanticscholar",
              "crossref"
            ]
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          "sortBy": {
            "title": "Sort Results By",
            "enum": [
              "relevance",
              "citations",
              "year",
              "sources"
            ],
            "type": "string",
            "description": "How to order the merged results. \"Relevance\" (default) ranks by source-overlap then citations then year — surfaces papers that multiple sources agreed on.",
            "default": "relevance"
          },
          "maxResults": {
            "title": "Maximum Results",
            "minimum": 1,
            "maximum": 500,
            "type": "integer",
            "description": "Maximum merged unique publications to return. Each source is queried for up to this many records before merging.",
            "default": 50
          },
          "includeAbstract": {
            "title": "Include Abstracts",
            "type": "boolean",
            "description": "Pull paper abstracts from OpenAlex / S2 / Crossref. Disable for faster, smaller responses.",
            "default": true
          },
          "extractKeywords": {
            "title": "Extract Keywords (TF-IDF)",
            "type": "boolean",
            "description": "Compute per-paper keywords from titles + abstracts using TF-IDF. Used by clustering and similarity. Free — no extra API calls. Auto-enabled in literatureReview / monitor / citationGraph / authorAnalysis modes.",
            "default": false
          },
          "cluster": {
            "title": "Cluster into Topics",
            "type": "boolean",
            "description": "Group results into topic clusters using keyword overlap (Jaccard similarity). Emits one `recordType: cluster` record per cluster. Auto-enabled in literatureReview / authorAnalysis modes.",
            "default": false
          },
          "classifyIntent": {
            "title": "Classify Paper Intent",
            "type": "boolean",
            "description": "Tag each paper with an intent type — method / survey / benchmark / theory / application / dataset / tool — derived from title patterns and reference count. Auto-enabled in every mode (set to false to skip).",
            "default": true
          },
          "includeCitations": {
            "title": "Include Forward Citations",
            "type": "boolean",
            "description": "For each result, fetch a list of papers that cite it (via Semantic Scholar). Adds one extra API call per top-N result (see enrichTopN). Slower and uses more credits.",
            "default": false
          },
          "includeReferences": {
            "title": "Include Backward References",
            "type": "boolean",
            "description": "For each result, fetch the list of papers it cites (via Semantic Scholar / Crossref). Adds one extra API call per top-N result.",
            "default": false
          },
          "citationLimit": {
            "title": "Citation / Reference Limit",
            "minimum": 1,
            "maximum": 100,
            "type": "integer",
            "description": "Maximum citations or references to attach per paper.",
            "default": 25
          },
          "enrichTopN": {
            "title": "Enrich Top N Only",
            "minimum": 1,
            "maximum": 500,
            "type": "integer",
            "description": "Only fetch citations/references for the top N ranked results. Saves API calls and credits when you only care about the most relevant papers.",
            "default": 25
          },
          "emitGraph": {
            "title": "Emit Citation Graph",
            "type": "boolean",
            "description": "Build a structured {nodes, edges} citation graph from the enriched records and compute PageRank to identify influential papers. Auto-enabled in citationGraph mode. Requires includeCitations and/or includeReferences.",
            "default": false
          },
          "emitNarrative": {
            "title": "Emit Markdown Summary",
            "type": "boolean",
            "description": "Append a `recordType: summary` record with an Executive Summary (key takeaways) + full Markdown narrative. Ready for emails, reports, or Notion. Auto-enabled in literatureReview / authorAnalysis modes.",
            "default": false
          },
          "emitInsights": {
            "title": "Emit Insights Record",
            "type": "boolean",
            "description": "Append a `recordType: insights` record with curated lists: top papers (best blend of quality + velocity + agreement), rising papers (highest citation velocity in last 5 years), foundational papers (≥1000 citations and ≥8 years old), controversial papers (heavy reference load), and emerging topics (recent + active clusters). Auto-enabled in literatureReview / authorAnalysis.",
            "default": false
          },
          "emitTrends": {
            "title": "Emit Cluster Trends",
            "type": "boolean",
            "description": "Compare current clusters against the prior run for the same scope and emit a `recordType: trends` record marking each cluster as new / rising (>25% growth) / stable / falling (>25% drop) / gone. Auto-enabled in monitor mode.",
            "default": false
          },
          "emitResearchBrief": {
            "title": "Emit Research Brief (default ON)",
            "type": "boolean",
            "description": "Append a single `recordType: researchBrief` canonical record that synthesizes everything: headline, one-line takeaway, key takeaways, what to read first, what's rising, what matters, topic landscape, breakout papers (high-impact + rising), enduring papers (old but still influential), recommended focus, and a confidence explanation. The hero output — set to false only if you only want the raw publication records.",
            "default": true
          },
          "emitAlerts": {
            "title": "Emit Alert Records",
            "type": "boolean",
            "description": "Emit `recordType: alert` records when threshold conditions cross — new high-impact papers, citation surges, seminal papers. Best paired with diffWithPriorRun on a schedule. Auto-enabled in monitor mode.",
            "default": false
          },
          "diffWithPriorRun": {
            "title": "Diff vs Last Run (Monitoring)",
            "type": "boolean",
            "description": "Compare current results against the prior run for the same scope (mode + query + filters). Adds `newSinceLastRun` and `citationDeltaSinceLastRun` to every paper. Auto-enabled in monitor mode. State is stored in a named KV store.",
            "default": false
          },
          "debug": {
            "title": "Debug Mode",
            "type": "boolean",
            "description": "Save raw per-source responses to KV store key `DEBUG_RAW_HITS` for inspection. Off by default to keep runs lean.",
            "default": false
          },
          "mailto": {
            "title": "Contact Email (recommended)",
            "type": "string",
            "description": "Used in API user-agent for OpenAlex and Crossref \"polite pool\" routing — gives faster, more reliable responses. Optional but recommended."
          }
        }
      },
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                    "example": "API"
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                  "userAgent": {
                    "type": "string"
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                  "inputBodyLen": {
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                  "DATASET_READS": {
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