"author:liftkkkk" matching MCP servers:

Enables AI assistants to query, modify, and reason over OWL/TTL/RDF ontology files using natural language, without needing SPARQL or OWL syntax.

Exposes deterministic, offline classical NLP capabilities — tokenization, POS tagging, NER, keyword extraction, sentence splitting, OpenIE triples, and sememe/WordNet lookup — as capability-level MCP tools that any LLM agent host can call. Every call returns a unified envelope with exact character offsets, honest engine attribution, and reproducible results, enabling cheap preprocessing, citation-grade spans, and two-stage extraction funnels that keep expensive model reasoning to a minimum.

Enables any MCP client (zcode, Claude Code, Cursor) to give its model bi-temporal long-term memory, auditable evidence-cited retrieval, LLM-assisted document extraction, and deterministic A* skill planning with precondition/effect verification. All four capabilities share one portable SQLite store and can be mounted independently on demand.

Enables MCP-capable AI clients to query journal evaluation data such as CAS partitions, JCR impact factors and quartiles, XinRui partitions, CCF recommended catalogs, and international warning lists, plus aggregate a full checkup of a journal across all sources. Also supports reverse journal recommendation from paper topic keywords and fuzzy journal name or ISSN lookup.