"Intelligent context compression techniques and methods" matching MCP servers:

Enables structured extraction of methods and reproducibility heuristics from academic papers, allowing AI agents to obtain metadata, full text, structured methods, code repository discovery, and a no-clone reproducibility verdict from a paper URL.

Enables AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.

Provides MCP tool adapters for Bioconductor methods like limma, DESeq2, and fgsea, enabling statistical analysis of omics data through containerized R execution. It serves as a bridge between MCP clients and bioinformatics tools for reproducible research workflows.

Compression AI - MCP server providing AI-powered tools and automation by MEOK AI Labs

Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

An MCP server that provides dynamic codebase context to Claude Code through tools like hybrid search, recent changes, and symbol definitions, enhancing AI-assisted coding with local RAG.

A local-first MCP server that ingests PDFs, extracts structure, and provides semantic search and sequential navigation tools for AI clients to query and learn from documents.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Durable project-memory MCP: decisions, constraints, and pipelines across Claude sessions

Enables LLMs to access a user's personal writing context—voice, style, opinions, expertise, projects, and communication patterns—via curated markdown files, helping the LLM match the user's voice when generating written content.

A Model Context Protocol server that gives AI assistants persistent, scoped memory by saving and retrieving short notes (mementos) across sessions and projects, enabling dynamic learning and context injection without heavy dependencies.

Context Forge is a vendor-neutral continuity layer for AI tools. It stores project context in Markdown and JSON, retrieves task-scoped context, and leaves structured handoffs so another model can continue without being taught the project again.

Enables MCP-capable clients to query coding-agent conversation exports and retrieve token-bounded evidence bundles with source provenance and hash verification, so LLMs can ground responses in verifiable history without replaying full transcripts.

Enables LLM agents to compress handoffs into structured, auditable context capsules, preserving goals, constraints, decisions, and risks without external API calls.

Local-first memory for AI agents about the people in your life. MCP server + CLI on SQLite. Never phones home.

Read-only repository context explorer for coding agents. Provides repository exploration tools via CLI or MCP adapter.

A unified context layer that connects your local data — repositories, documents, remote machines, and notes — to LLM interfaces through the Model Context Protocol (MCP).

A task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.

Context Portal (ConPort): A memory bank MCP server building a project-specific knowledge graph to supercharge AI assistants. Enables powerful Retrieval Augmented Generation (RAG) for context-aware development in your IDE.