"A server for finding information about context" matching MCP servers:

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.

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 daily feed of AI releases, tools and techniques, each with a verification status, source trust score and evidence links. Read-only MCP server, no sign-in or keys.

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.

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

Enables users to audit personal reading streams by quantifying category diversity with Shannon entropy, detecting lexical redundancy and echo chambers via Jaccard similarity, and ranking content sources by decibel signal-to-noise ratio for pruning recommendations.

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.

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.

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

Universal HTTP 402 Micropayment Protocol & Autonomous Economic Settlement Engine for AI Agents on Base L2.

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.

Answers why a specific number in a Chinese listed company moved — margins, cash flow, inventory, management commentary, policy impact — by reading filed reports and IR transcripts and returning figures with the document and announcement number attached. It deliberately refuses price, valuation, and recommendation queries, returning a "gone_by_design" error instead.

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.

Enables searching and retrieving content, module information, and Twig template snippets from a-blog cms via its GET API.

Lets an AI assistant read call and message history, send texts and picture messages, reach Phone Book contacts by name, and — once switched on — place calls run by a voice agent, all limited to the phone numbers the user ticks. Hosted remotely with OAuth sign-in, so nothing needs to be installed locally.

Enables AI assistants to save, load, and search conversation context with AI-powered summarization and auto-tagging. Demonstrates semantic intent patterns and hexagonal architecture for maintainable AI-assisted development.

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.

Enables inspection and governance of enterprise ingested context by classifying security clearance tiers, sanitizing cross-department prompts, and enforcing role-based access boundaries to prevent data leaks. It runs as a zero-dependency MCP server compatible with Claude Desktop, Cursor, and other agent frameworks.

Enables long-horizon autonomous agents to avoid context-window exhaustion by enforcing configurable token budgets and distilling older conversation turns into concise sliding summaries.

MCP-first A/B testing server that enables agents to manage experiments (create, update traffic splits, read results, apply winning variants) from tools like Claude Code, with self-hosted Cloudflare backend.

Reference MCP server for OCL, an open standard giving AI agents structured, scoped business meaning for Odoo data. Serves published example context packs via get_context, resolve_noun, and explain_field (stdio, Python, Apache-2.0).

Enables long-horizon conversational memory compaction and episodic anchor extraction for AI agents, providing deterministic memory management through a zero-dependency MCP server.

GenPark MCP server for selecting relevant context under token and latency constraints.

Enables AI agents and MCP-compliant clients to dynamically shrink long-horizon context windows, pruning verbose tool outputs, HTML boilerplate, and semantic redundancy by roughly 40-70% without frontier LLM round-trips. Runs deterministically as a zero-dependency Python stdio server, accepting a payload plus optional compression and risk-bound options.

Enables MCP-compatible clients and autonomous agents to dynamically compress long-horizon context by pruning verbose tool outputs, HTML boilerplate, and semantic redundancy by 40-70%. It reduces token burn and latency through deterministic, zero-dependency processing.