Research Tracker MCP Server

""" Research Tracker MCP Server - MCP Tool Functions Public-facing MCP tool functions with rate limiting and error handling. """ import logging from typing import List, Dict, Any from config import logger from utils import rate_limit from inference import ( create_row_data, infer_paper_from_row, infer_name_from_row, infer_code_from_row, infer_authors_from_row, infer_date_from_row, infer_model_from_row, infer_dataset_from_row, infer_space_from_row, infer_license_from_row, infer_field_type ) logger = logging.getLogger(__name__) # MCP tool functions @rate_limit("mcp_tools") def infer_authors(input_data: str) -> List[str]: """ Infer authors from research paper or project information. This tool extracts author names from: - arXiv papers (via API) - HuggingFace paper pages (via scraping) - GitHub repositories (via API when GITHUB_AUTH is set) Args: input_data (str): A URL, paper title, or other research-related input. Examples: - "https://arxiv.org/abs/2103.00020" - "https://huggingface.co/papers/2103.00020" - "https://github.com/openai/CLIP" Returns: List[str]: A list of author names as strings, or empty list if no authors found. Example: ["Alec Radford", "Jong Wook Kim", "Chris Hallacy"] Raises: ValidationError: If input_data is invalid or malformed ExternalAPIError: If external API calls fail after retries """ if not input_data or not input_data.strip(): return [] try: cleaned_input = input_data.strip() row_data = create_row_data(cleaned_input) authors = infer_authors_from_row(row_data) valid_authors = [] for author in authors: if isinstance(author, str) and len(author.strip()) > 0: cleaned_author = author.strip() if 2 <= len(cleaned_author) <= 100: valid_authors.append(cleaned_author) logger.info(f"Successfully inferred {len(valid_authors)} authors from input") return valid_authors except Exception as e: logger.error(f"Error inferring authors: {e}") return [] @rate_limit("mcp_tools") def infer_paper_url(input_data: str) -> str: """ Infer the paper URL from various research-related inputs. This tool finds paper URLs by: - Validating existing paper URLs - Searching GitHub repositories for paper links - Converting between arXiv and HuggingFace paper formats - Searching by paper title when provided Args: input_data (str): A URL, repository link, or other research-related input Examples: - "https://github.com/openai/CLIP" - "Vision Transformer" - "https://huggingface.co/spaces/example" Returns: str: The paper URL (typically arXiv or Hugging Face papers), or empty string if not found Example: "https://huggingface.co/papers/2103.00020" """ if not input_data or not input_data.strip(): return "" try: row_data = create_row_data(input_data.strip()) result = infer_paper_from_row(row_data) return result or "" except Exception as e: logger.error(f"Error inferring paper: {e}") return "" @rate_limit("mcp_tools") def infer_code_repository(input_data: str) -> str: """ Infer the code repository URL from research-related inputs. This tool discovers code repositories by: - Scraping HuggingFace paper pages for GitHub links - Searching GitHub for repositories by paper title - Extracting repository links from project pages Args: input_data (str): A URL, paper link, or other research-related input Examples: - "https://arxiv.org/abs/2010.11929" - "https://huggingface.co/papers/2010.11929" - "Vision Transformer" Returns: str: The code repository URL (typically GitHub), or empty string if not found Example: "https://github.com/google-research/vision_transformer" """ if not input_data or not input_data.strip(): return "" try: row_data = create_row_data(input_data.strip()) result = infer_code_from_row(row_data) return result or "" except Exception as e: logger.error(f"Error inferring code: {e}") return "" def infer_research_name(input_data: str) -> str: """ Infer the research paper or project name from various inputs. Args: input_data (str): A URL, repository link, or other research-related input Returns: str: The research name/title, or empty string if not found """ if not input_data or not input_data.strip(): return "" try: row_data = create_row_data(input_data.strip()) result = infer_name_from_row(row_data) return result or "" except Exception as e: logger.error(f"Error inferring name: {e}") return "" @rate_limit("mcp_tools") def classify_research_url(input_data: str) -> str: """ Classify the type of research-related URL or input. This tool identifies resource types based on URL patterns: - Paper: arXiv, HuggingFace papers, PDF files - Code: GitHub repositories - Model: HuggingFace model pages - Dataset: HuggingFace dataset pages - Space: HuggingFace space/demo pages - Project: GitHub.io pages - Unknown: Unrecognized patterns Args: input_data (str): The URL or input to classify Examples: - "https://arxiv.org/abs/2103.00020" -> "Paper" - "https://github.com/openai/CLIP" -> "Code" - "https://huggingface.co/openai/clip-vit-base-patch32" -> "Model" Returns: str: The field type: "Paper", "Code", "Space", "Model", "Dataset", "Project", or "Unknown" """ if not input_data or not input_data.strip(): return "Unknown" try: field = infer_field_type(input_data) return field if field else "Unknown" except Exception as e: logger.error(f"Error classifying URL: {e}") return "Unknown" def infer_publication_date(input_data: str) -> str: """ Infer publication date from research paper or project information. Args: input_data (str): A URL, paper title, or other research-related input Returns: str: Publication date as string (YYYY-MM-DD format), or empty string if not found """ if not input_data or not input_data.strip(): return "" try: row_data = create_row_data(input_data.strip()) result = infer_date_from_row(row_data) return result or "" except Exception as e: logger.error(f"Error inferring publication date: {e}") return "" def infer_model(input_data: str) -> str: """ Infer associated HuggingFace model from research paper or project information. Args: input_data (str): A URL, paper title, or other research-related input Returns: str: HuggingFace model URL, or empty string if no model found """ if not input_data or not input_data.strip(): return "" try: row_data = create_row_data(input_data.strip()) result = infer_model_from_row(row_data) return result or "" except Exception as e: logger.error(f"Error inferring model: {e}") return "" def infer_dataset(input_data: str) -> str: """ Infer associated HuggingFace dataset from research paper or project information. Args: input_data (str): A URL, paper title, or other research-related input Returns: str: HuggingFace dataset URL, or empty string if no dataset found """ if not input_data or not input_data.strip(): return "" try: row_data = create_row_data(input_data.strip()) result = infer_dataset_from_row(row_data) return result or "" except Exception as e: logger.error(f"Error inferring dataset: {e}") return "" def infer_space(input_data: str) -> str: """ Infer associated HuggingFace space from research paper or project information. Args: input_data (str): A URL, paper title, or other research-related input Returns: str: HuggingFace space URL, or empty string if no space found """ if not input_data or not input_data.strip(): return "" try: row_data = create_row_data(input_data.strip()) result = infer_space_from_row(row_data) return result or "" except Exception as e: logger.error(f"Error inferring space: {e}") return "" def infer_license(input_data: str) -> str: """ Infer license information from research repository or project. Args: input_data (str): A URL, repository link, or other research-related input Returns: str: License name/type, or empty string if no license found """ if not input_data or not input_data.strip(): return "" try: row_data = create_row_data(input_data.strip()) result = infer_license_from_row(row_data) return result or "" except Exception as e: logger.error(f"Error inferring license: {e}") return ""