"Abstract" matching MCP servers:

Reusable MCP server for Python backend development with FastAPI, MongoDB, and Stackraise support.

Enables LLM-driven text game state management by exposing MCP tools for managing players, locations, items, entities, and abstract concepts.

Enables fetching HuggingFace daily papers for today, yesterday, or a specific date, providing paper details including title, authors, abstract, tags, votes, and links.

Provides advanced code structure and semantic analysis through Abstract Syntax Trees (AST) and Abstract Semantic Graphs (ASG) across multiple programming languages. It enables tasks like incremental parsing, complexity analysis, and AST diffing to help models understand and navigate codebases.

Provides medical symptom extraction, differential diagnosis generation, PubMed literature search, and abstract summarization tools for LLM agents.

Enables AI-assisted aquaculture manuscript writing through six specialized agents for literature review, drafting, results, abstract, copyediting, and integrity checks, all accessible from MCP-compatible chat clients.

Enables AI assistants to search and analyze codebases using Abstract Syntax Tree (AST) pattern matching with ast-grep. Supports structural code search, pattern testing, and AST visualization across multiple programming languages.

MCP server for Neo4j that provides abstract graph operations for LLMs, enabling safe and consistent interaction with Neo4j databases through tools like search, insert, update, delete, and schema introspection.

Enables searching and retrieving detailed information from PubMed articles using the NCBI Entrez API. Supports configurable search parameters including title/abstract filtering and keyword expansion to find relevant scientific publications.

MCP server for scientific grounding: search and discover open-access research papers across arXiv, OpenAlex, Crossref, PubMed, and Semantic Scholar, and retrieve references/citations from paywalled journals via public DOI/abstract metadata.

A robust, language-agnostic Model Context Protocol (MCP) server that provides AI coding agents with the ability to edit files surgically via Abstract Syntax Trees (AST) instead of relying on token-heavy, brittle search-and-replace or diff operations.

abstractapi-mcp-server

This MCP server enables AI models to analyze local Python codebases using abstract syntax trees, providing tools for file structure analysis, symbol search, import graphing, docstring auditing, and refactoring prompts without loading entire source files into context.

Enables read-only searching of CNKI literature metadata by topic, keyword, title, author, abstract, or DOI, with filters for year and publication type plus relevance, date, citation, and download sorting. Also retrieves full paper details such as authors, institutions, abstracts, keywords, and references, helping models check whether a research topic has already been studied.