"A resource for understanding Claude's context window and capabilities" matching MCP servers:

Enables agents to optimize context-window attention by reordering retrieved documents so critical evidence lands at prompt boundaries, mitigating lost-in-the-middle degradation. It combines BM25 lexical scoring, dense vector similarity, reciprocal rank fusion, knowledge-graph expansion, and recency-decay memory caching, exposed as a zero-dependency MCP stdio server.

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.

Enables async-first working practices by providing tools to draft decision docs, convert meetings to async artifacts, score status updates, and triage sync vs async tasks. It also offers reference tools for the Open and Async book's principles and coaching prompts.

A context window optimizer and session rotator MCP server for agentic workflows that prevents LLMs from running out of context by compacting chat history and rotating sessions.

Enables AI agents to browse, trade, and create prediction markets on Context Markets. Supports wallet management, order placement, and market creation through MCP tools.

A local MCP server that acts as a policy-based firewall for personal context, letting users control agent access to their data with deny-by-default rules, per-field grants, and an audit trail.

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

Enables AI agents to compose MCP tool calls into Unix-style pipelines, executing complex workflows server-side and returning only the final result.

Graph RAG based context management engine for coding agents

Enables semantic search of project documentation using hybrid vector and full-text search with fast and deep query modes for immediate results or complex multi-round synthesis.

Enables AI agents to perform corpus analysis tasks such as discovery, search, and data access via the Model Context Protocol.

Indexes your codebase so AI coding agents can search instead of re-reading files, saving up to 94% of tokens.

Enables querying context about the MAM product components and architecture through MCP, allowing agents to retrieve targeted product information on demand instead of loading full documentation.

MCP server providing persistent memory for LLMs via semantic search over captures, structured facts with temporal tracking, and document ingestion. Enables any MCP-compatible client to maintain a searchable knowledge base across sessions.

This MCP wraps Bitrefill public API to allow agents to search for products and shop using cryptocurrencies like Bitcoin, Ethereum, Solana, and many more.

Credit and payments for AI agent developers — over MCP. No crypto required

MCP server for answering questions about official statistics from a single publication URL, with tools to check query scope, retrieve overview, text, data files, and search.

lets you interact with your meeting data (to list, ask, summarize, extract - virtually anything about your meeting) through Claude’s desktop client across Zoom, Google Meet and MS Teams via tl;dv.

Provides Stackpress framework context and documentation to AI utilities like Claude Desktop and Cline. Enables AI assistants to understand and work with Stackpress projects by fetching and serving relevant framework information.

A client-side MCP proxy that injects OAuth 2.0 bearer tokens or API keys into MCP requests, enabling MCP clients to connect to OAuth/API-key-protected MCP servers like Amazon Bedrock AgentCore Gateway. It automatically fetches and refreshes credentials using AgentCore Identity or static values.