"A resource for understanding Claude's context window and how it works" matching MCP servers:

Manages resource allocation, MCP server lifecycle, and Kubernetes workers in cortex automation systems. Provides tools for requesting/releasing job resources, starting/stopping/scaling MCP servers, and provisioning/destroying burst workers with TTL management.

Enables governed Codex work sessions by planning bounded segments, maintaining capacity reserves, and creating checkpoint-based resume contracts so long-running jobs can pause and resume deterministically within quota limits.

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.

Provides MCP tools to profile and optimize LLM conversation context, identifying token waste and applying deterministic fixes to reduce context window usage.

Enables AI coding agents to request a second opinion from a different frontier model via a single tool call, with PII scrubbed before transit, live web search, and support for Claude, ChatGPT, Cursor, and Gemini.

Enables AI agents to control a Playwright-driven browser through natural-language instructions, including page interaction and live browser task execution.

Enables AI agents to request an independent second opinion from a different frontier model, with PII scrubbing before transit, live web search grounding, caching, and multi-provider failover.

Enables autonomous context window management, prompt caching, token optimization, and real-time observability for AI coding agents across multiple IDEs, reducing token costs and improving performance.

Context window usage estimation for AI coding agents via MCP, enabling proactive state preservation before compaction.

MCP server providing lossless file reads with deduplication, per-repo token metering, and HMAC-signed context receipts. Enables auditable, vendor-neutral measurement of what an AI agent saw.

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

A reusable MCP server providing shared, versioned context across AI agents and devices via a private GitHub workspace, with tools to discover projects, bootstrap, query, and close out task state.

Aggregator MCP proxy that collapses N downstream MCP servers into 4 meta-tools with progressive tool discovery, and compresses large tool outputs (HTML→Markdown, JSON summarization) with full-output retrieval via read_more and a per-session token-savings report.

Provides persistent memory, identity, and context for Claude sessions through a memory graph, enabling recall, storage, and relationship management of entities and decisions.

Enables multiple AI coding agents and humans to coordinate through a Git-native Markdown blackboard (.context/ + AGENTS.md), where they can inspect workspace overviews, claim and toggle ready tasks, record architecture decisions, synthesize shared prompts, and hand off work via atomic, lock-protected writes that prevent lost updates and formatting drift. It also serves a fully offline local web dashboard that live-syncs any blackboard change over WebSocket.

MCP server providing virtual memory for AI context with durable SQLite storage, retrieval, and bounded prompt assembly to manage context windows.

Enables ZCode to control a real Chrome browser, including tabs, navigation, page content, clicks, form filling, keyboard input, scrolling, screenshots, and JavaScript evaluation through a local MCP bridge.

MCP proxy that reduces context usage through semantic tool routing, enabling on-demand discovery and routing of relevant tools.

This MCP server provides tools to manage, score, compress, and prune AI agent conversation context, helping keep agents focused and reduce token costs. It is a free, local, pure Python solution for any MCP client.

Reduces token consumption for AI coding agents by 50-70% through intelligent code context filtering, Git delta tracking, and local SQLite/Tree-sitter indexing.