"Automatic context compression tools or methods" matching MCP servers:

Drives real COMSOL Multiphysics sessions through natural language over MCP, covering geometry, materials, physics fields, coupling, meshing, studies, solving, and result visualization via 99 tools with session protection, serial execution, and HTTP authentication. Also provides an original 3D heat-sink example and curated classic case recipes across physics domains such as AC/DC, acoustics, heat transfer, structural mechanics, and optics.

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 agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.

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.

Recommends study designs for a research question, states the identifying assumptions to verify, and returns runnable R, Python, or Julia code with a formatted citation for every source, drawn from methodological textbooks, an R package, and research articles on causal inference, econometrics, statistics, and machine learning.

MCP server for local compression of various image formats

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

MCP server that provides OpenRouter model pricing data, enabling price lookups, trending/cheapest lists, and model searches without an API key.

A Model Context Protocol (MCP) server designed to easily dump your codebase context into Large Language Models (LLMs).

Provides live wait times and office details for all 60 Oregon DMV field offices by scraping the ODOT website, with no authentication required.

Lets AI agents inspect local or remote videos by reading metadata, detecting shots and cuts, extracting frames as images, and checking keyframes for seeking and streaming, all via local FFmpeg without an API key.

Provides live web and document intelligence, including search, company news, scraping, crawling, structured extraction, document parsing, brand intelligence, screenshots, website monitoring, and batch jobs.

A lightweight MCP server for semantic search over markdown knowledge bases, enabling AI coding agents to index, search, and answer questions from local markdown documents.

Enables AI clients to read and write to a user-owned markdown bucket via S3-compatible storage, providing a persistent shared context layer across different AI tools.

MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to: Submit and validate constraint models Set model parameters Solve constraint satisfaction and optimization problems Retrieve and analyze solution

Local-first documentation for AI agents

Enables AI agents and MCP clients to compress retrieved web documents against a query, pruning irrelevant noise through deterministic algorithmic scoring so downstream context stays lean. It exposes this capability over the Model Context Protocol for integration with Claude Desktop, Cursor, and similar clients.

Enables AI agents to compress retrieved web documents against a query, pruning irrelevant noise so only the most relevant context reaches the model. It runs as a zero-dependency Python MCP server that accepts a query payload and returns structured, scored output.

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.

Context Mode is an MCP server that reduces context window waste by sandboxing data-heavy tools, tracking session state in SQLite, and promoting code-based analysis over raw data reads, achieving up to 98% context savings.