Airesearch

Airesearch implements a Model Context Protocol (MCP) server that handles bidirectional communication between Claude (or other MCP-compatible clients) and research tools. It manages server initialization, request routing, resource discovery, and graceful shutdown using the MCP specification's transport layer (stdio, SSE, or custom). The server exposes tools and resources through MCP's standardized schema, allowing clients to dynamically discover and invoke capabilities without hardcoded integrations.

Airesearch provides semantic search capabilities to discover research papers from academic databases (likely arXiv, PubMed, or similar) using natural language queries. It converts user queries into embeddings and matches them against indexed paper metadata and abstracts, returning ranked results with relevance scores. The implementation likely uses vector similarity search (cosine distance or similar) against pre-indexed embeddings, enabling researchers to find papers without learning database-specific query syntax.

Airesearch extracts structured content from research papers (title, authors, abstract, methodology, results, conclusions) and generates summaries at multiple granularity levels (abstract-only, key findings, full paper summary). It likely uses PDF parsing with layout-aware extraction (e.g., pdfplumber or similar) combined with LLM-based summarization to produce coherent, hierarchical summaries that preserve research intent. The extraction preserves citations and references for downstream analysis.

Airesearch enables traversal of citation networks to discover related papers, influential works, and research lineage. It implements graph traversal algorithms (BFS, DFS, or PageRank-style citation weighting) to find papers that cite a given work, papers cited by a work, and papers in the same citation cluster. The implementation likely queries citation indices (CrossRef, Semantic Scholar API, or similar) and builds transitive relationships, allowing researchers to explore research genealogy and impact.

Airesearch discovers research datasets from repositories (Kaggle, Zenodo, Figshare, or domain-specific repositories) using semantic search and metadata matching. It extracts dataset metadata (size, format, license, description, citation information) and provides access to dataset documentation and schemas. The implementation queries dataset indices and parses repository APIs to provide standardized dataset information regardless of source repository.

Airesearch analyzes and compares methodologies across multiple papers, extracting methodology descriptions, parameters, and results to enable systematic comparison. It uses structured extraction (likely with LLM-based parsing) to identify methodology components (data preprocessing, model architecture, training procedures, evaluation metrics) and creates comparison matrices. The synthesis capability identifies common patterns, variations, and trade-offs across methodologies, helping researchers understand the landscape of approaches.

Airesearch assists in generating research hypotheses based on literature analysis and planning validation experiments. It analyzes existing research to identify gaps, contradictions, and unexplored areas, then suggests hypotheses grounded in literature. The validation planning capability outlines experiment designs, required datasets, and evaluation metrics based on similar studies. This uses reasoning patterns (gap analysis, contradiction identification) combined with research knowledge to suggest novel research directions.

Airesearch discovers and analyzes code repositories associated with research papers (GitHub, Zenodo, supplementary materials) to verify reproducibility and extract implementation details. It parses repository metadata, identifies code language and dependencies, and extracts key implementation components. The verification capability checks for documentation, test coverage, and dependency specifications to assess reproducibility maturity. This enables researchers to evaluate whether papers provide sufficient code and data for reproduction.