paperpilot-omega.vercel.app

{
  "name": "search_papers",
  "kind": "answer",
  "impl": "imperative",
  "description": "Search arXiv for academic papers by topic, date range, venue, and citation filters. Returns structured paper metadata including title, authors, abstract, and arXiv ID.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "query": {
        "type": "string",
        "description": "Search keywords or natural language topic"
      },
      "date_range": {
        "type": "object",
        "properties": {
          "from": {
            "type": "string",
            "format": "date",
            "description": "Start date (YYYY-MM-DD)"
          },
          "to": {
            "type": "string",
            "format": "date",
            "description": "End date (YYYY-MM-DD)"
          }
        }
      },
      "venues": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Target conferences or journals"
      },
      "min_citations": {
        "type": "integer",
        "minimum": 0,
        "description": "Minimum citation count filter"
      },
      "max_results": {
        "type": "integer",
        "minimum": 1,
        "maximum": 20,
        "default": 10,
        "description": "Maximum papers to return"
      }
    },
    "required": [
      "query"
    ]
  }
}

{
  "name": "extract_findings",
  "kind": "act",
  "impl": "imperative",
  "description": "Extract structured findings from a specific paper: research question, methodology, key claims, limitations, and conclusion summary. Grounded in the paper's full text when available.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "paper_id": {
        "type": "string",
        "description": "arXiv ID of the paper to analyze"
      },
      "extraction_depth": {
        "type": "string",
        "enum": [
          "brief",
          "detailed"
        ],
        "default": "detailed",
        "description": "Level of detail for extraction"
      }
    },
    "required": [
      "paper_id"
    ]
  }
}

{
  "name": "find_evidence",
  "kind": "answer",
  "impl": "imperative",
  "description": "Search a paper's full text for evidence matching a query. Returns the most relevant quoted sentences with relevance scores. Use this to answer specific factual questions (exact numbers, methods, datasets, findings) that go beyond the extracted findings summary — including answering the user's follow-up questions about a paper.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "paper_id": {
        "type": "string",
        "description": "arXiv ID of the paper to search"
      },
      "query": {
        "type": "string",
        "description": "What to look for, e.g. \"p95 latency overhead\" or \"evaluation setup\""
      },
      "max_results": {
        "type": "integer",
        "minimum": 1,
        "maximum": 10,
        "default": 5
      }
    },
    "required": [
      "paper_id",
      "query"
    ]
  }
}

{
  "name": "compare_papers",
  "kind": "act",
  "impl": "imperative",
  "description": "Generate a structured comparison matrix across multiple papers on specified dimensions (methodology, results, limitations, threat model).",
  "inputSchema": {
    "type": "object",
    "properties": {
      "paper_ids": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "List of arXiv IDs to compare"
      },
      "dimensions": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Comparison dimensions"
      }
    },
    "required": [
      "paper_ids",
      "dimensions"
    ]
  }
}

{
  "name": "find_related",
  "kind": "answer",
  "impl": "imperative",
  "description": "Discover semantically related papers to a given paper based on citation overlap and keyword similarity.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "paper_id": {
        "type": "string",
        "description": "arXiv ID of the source paper"
      },
      "relation_type": {
        "type": "string",
        "enum": [
          "citations",
          "semantic_neighbors"
        ],
        "default": "semantic_neighbors"
      },
      "max_results": {
        "type": "integer",
        "minimum": 1,
        "maximum": 10,
        "default": 5
      }
    },
    "required": [
      "paper_id"
    ]
  }
}

{
  "name": "add_to_collection",
  "kind": "act",
  "impl": "imperative",
  "description": "Add a paper to a named collection with user annotation and relevance rating. Creates collection if it does not exist.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "paper_id": {
        "type": "string",
        "description": "arXiv ID of the paper to save"
      },
      "collection_name": {
        "type": "string",
        "description": "Name of the collection"
      },
      "annotation": {
        "type": "string",
        "description": "User-written notes about this paper"
      },
      "relevance_rating": {
        "type": "integer",
        "minimum": 1,
        "maximum": 5,
        "description": "Relevance score 1-5"
      }
    },
    "required": [
      "paper_id",
      "collection_name"
    ]
  }
}

{
  "name": "get_collection",
  "kind": "answer",
  "impl": "imperative",
  "description": "Retrieve a collection by name with all saved papers, annotations, and ratings.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "collection_name": {
        "type": "string",
        "description": "Name of the collection to retrieve"
      }
    },
    "required": [
      "collection_name"
    ]
  }
}

{
  "name": "generate_outline",
  "kind": "act",
  "impl": "imperative",
  "description": "Generate a structured paper outline from a collection — supports literature reviews, research articles (with Abstract), and thesis chapters.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "collection_name": {
        "type": "string",
        "description": "Collection to base outline on"
      },
      "paper_type": {
        "type": "string",
        "enum": [
          "literature_review",
          "research_article",
          "thesis_chapter"
        ],
        "default": "literature_review"
      }
    },
    "required": [
      "collection_name"
    ]
  }
}

{
  "name": "draft_section",
  "kind": "act",
  "impl": "imperative",
  "description": "Draft a paper section using template-based synthesis from collection papers. Inserts {{paperId}} citation placeholders for human verification. For higher-quality prose, compose the section in chat with the user and save it with revise_section.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "section_id": {
        "type": "string",
        "description": "ID of the section to draft"
      },
      "collection_name": {
        "type": "string",
        "description": "Collection to source from"
      },
      "tone": {
        "type": "string",
        "enum": [
          "academic",
          "critical",
          "synthesis"
        ],
        "default": "academic"
      }
    },
    "required": [
      "section_id",
      "collection_name"
    ]
  }
}

{
  "name": "revise_section",
  "kind": "act",
  "impl": "imperative",
  "description": "Replace the editable text of a paper section with revised prose — use this to save text the agent composed in chat after user feedback, so the human can review it in the app and export it. Sets the section status to \"editing\" for human review.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "section_id": {
        "type": "string",
        "description": "ID of the section to update"
      },
      "new_text": {
        "type": "string",
        "description": "The full revised prose for this section"
      }
    },
    "required": [
      "section_id",
      "new_text"
    ]
  }
}

{
  "name": "insert_citation",
  "kind": "act",
  "impl": "imperative",
  "description": "Resolve {{paperId}} placeholders to linked in-text citations and update the bibliography. The student chooses APA or IEEE when exporting.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "section_id": {
        "type": "string",
        "description": "Section containing placeholders"
      },
      "collection_name": {
        "type": "string",
        "description": "Collection with paper metadata"
      }
    },
    "required": [
      "section_id",
      "collection_name"
    ]
  }
}

{
  "name": "verify_claim",
  "kind": "answer",
  "impl": "imperative",
  "description": "Verify that a claim in a section is supported by the cited paper. Checks against the paper's full text and returns the closest quoted source sentence with an overlap score.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "section_id": {
        "type": "string",
        "description": "Section containing the claim"
      },
      "claim_text": {
        "type": "string",
        "description": "The specific claim to verify"
      },
      "paper_id": {
        "type": "string",
        "description": "arXiv ID of the cited paper"
      }
    },
    "required": [
      "section_id",
      "claim_text",
      "paper_id"
    ]
  }
}

{
  "name": "suggest_transition",
  "kind": "act",
  "impl": "imperative",
  "description": "Generate a bridge sentence connecting two consecutive sections for smooth paper flow.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "from_section_id": {
        "type": "string",
        "description": "ID of the preceding section"
      },
      "to_section_id": {
        "type": "string",
        "description": "ID of the following section"
      }
    },
    "required": [
      "from_section_id",
      "to_section_id"
    ]
  }
}