"author:78" matching MCP servers:

Author rules from policy docs, then decide: a Rete engine gives the verdict, an LLM explains why.

An MCP server that provides access to Semantic Scholar's academic graph, recommendations, and datasets APIs, enabling paper search, citation analysis, author lookups, and dataset discovery through 20+ tools.

Entroly is a high-performance context engineering engine that optimizes AI coding agent context windows through information-theoretic compression and reinforcement learning. It gives your AI 100% codebase visibility while reducing token consumption by an average of 78%.It's learns fully autonomously.

Enables comprehensive academic research through the Semantic Scholar API, including paper search, author discovery, citation network analysis, and full-text access from arXiv and Wiley sources.

Provides 78 tools for AGI experiments based on universal physics principles like entropy and criticality. It enables advanced long-term memory management, diversity-focused information retrieval, and meta-cognitive monitoring for self-improving AI systems.

A comprehensive Model Context Protocol server that provides AI assistants with direct access to Semantic Scholar's academic database, enabling advanced paper discovery, citation analysis, author research, and AI-powered recommendations.

Enables semantic search and ingestion of investor content using Upstash Vector and Vercel Blob storage, with optional filtering by author, company, and date.

Local-first agent memory & knowledge service: hybrid retrieval (vector + BM25/RRF) + memory governance (decay, dedup, freshness), REST + MCP dual protocol, fully offline without LLM. Python.

Enables agents to explore a codebase as a graph via MCP, searching for relevant functions and their connections, and retrieving cited code chunks with callers, callees, and neighbours.

Persistent AI memory for Claude Code, Cursor, GitHub Copilot & Windsurf — sessions, lessons learned, semantic search, and team brain. 38 MCP tools. Free tier, EU servers.

Enables AI agents to fetch one or many web pages concurrently and receive clean, LLM-ready Markdown with navigation, ads and boilerplate stripped away, plus extracted metadata such as title, description, author, Open Graph tags and canonical URL. It runs locally over stdio with no API key or account, requiring only a single command.

Provides persistent context management for AI agents by storing and querying semantic information using Upstash Vector DB and Google AI embeddings. It enables semantic search, batch operations, and metadata filtering to help agents retrieve relevant stored knowledge.

Combines a knowledge graph with RAG (Retrieval-Augmented Generation) capabilities for semantic code indexing and search. Enables creating entity relationships, managing observations, and performing semantic searches across indexed codebases.

Gives any MCP client 27 tools for turning scattered feeds, forums, and media into a persistent, searchable evidence corpus, so an agent can search and import evidence, verify claims with independent-source counting and citation checks, and run multi-round research that produces cited, revisable reports. It also supports adaptive retrieval widening, tiered evidence where only author-written sections count as sources, and honest degradation when no LLM key is available.

Provides an MCP protocol interface for interacting with Elasticsearch 7.x databases, supporting comprehensive search functionality including aggregations, highlighting, and sorting.

Enables semantic and keyword search across locally indexed PDF shelves—device manuals, meeting minutes, and scientific papers—with date, device, and author filters, returning structured citations and neighbouring passage context for a speech assistant. It also exposes shelf and document metadata, plus a reindex signal, over streamable HTTP backed by Postgres with pgvector.

A local MCP server that lets agents search shared skills hosted in public or private git repos, showing ranked matches with author and dates, and load a chosen one only for the current session. It indexes those repos locally, so local skills stay untouched and shared skills are fetched on demand.

Unifies LAN agent memory (Postgres + pgvector + Ollama) with an Obsidian vault adapter, exposing tools for semantic recall, knowledge base search/write, and memory management.

Provides AI assistants with contextual access to Berlin Group Open Finance API specifications, enabling specification-compliant guidance through semantic search, graph database queries, and document retrieval.