"Information about cursors (general use or specific context)" matching MCP servers:

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 Claude to index and retrieve context from codebases using self-hosted Milvus for semantic search, with hardened reliability and security for production use.

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.

Enables LLM assistants to store, retrieve, and update user-specific context memory including travel preferences and general information through a chat interface. Provides analytics on tool usage patterns and token costs for continuous improvement.

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 semantic search across documentation stored in Gemini FileSearchStores, returning AI-generated answers with source citations.

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.

Enables users to search locally across imported Claude, ChatGPT, and Claude Code conversations and retrieve surrounding turns with citations, while keeping indexing and storage on their own computer.

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.

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.

Provides an external context buffer so a local model can maintain session memory across long tasks and search and page through large documents without loading them whole into its limited context window.

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.

Give your AI agents access to 8,500+ community curated awesome lists with over 1 million curated resources.

Enables AI agents to parse PDFs and search local document context through multimodal extraction and vector-based retrieval, all exposed as MCP tools.

Proxy for OpenAI-compatible backends that compacts conversation history before the context window fills, with read-only MCP tools to search and read back the archived originals.

An MCP server that enables processing of massive datasets up to 10M+ tokens using a recursive language model pattern for strategic chunking and analysis. It automates sub-queries and result aggregation using free local inference via Ollama or the Claude API to handle context beyond standard prompt limits.

Deterministic, local-first context optimization for AI coding agents. Automatically compresses raw development session data (messages, tool logs, error stack traces, and git diffs) into a compact, task-specific context window. Operates 100% locally with zero external LLM dependencies, ensuring your source code never leaves your machine.

Provides persistent context management for AI agents by storing and querying semantic information using Upstash Vector DB and Google AI embeddings. It enables semantic search, batch operations, and metadata filtering to help agents retrieve relevant stored knowledge.

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 local-first context retrieval and management from JSONL and Markdown sources through MCP, with a rebuildable full-text search index and support for stable reference pagination.

Enables engineers and AI assistants to search source-backed hardware design guidelines, retrieve detailed conditions and limitations, and trace citations to original sources, all through a read-only offline interface.

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.

An MCP server that enables AI assistants to manage Context Repo prompts, documents, and collections with semantic search and progressive disclosure navigation.

A self-hostable remote MCP server that gives multiple AI agents shared, durable, user-owned memory with semantic search, versioned documents, reusable skills, and lessons. It keeps each account isolated while running on the user's own Cloudflare deployment.

Lets AI agents retrieve source-linked passages from a local folder of Markdown notes or an Obsidian vault, optionally following the explicit links between notes, and can also fetch passages from pinned public GitHub files you configure. It returns bounded evidence packets with original text, file paths and hashes so agents can answer from your material rather than generating content themselves.

Exposes an Agent Context Graph and ContextPod contract over MCP so agents can describe, resolve, and inspect context revisions and events for a pod. It also lets clients append events while applying required semantic steps such as redaction, cache invalidation, supersession, conflict escalation, and release eligibility.

Enables agents to pull compact MySQL row diffs into their context, supporting before/after changes via triggers or watermark-based updates, with tools to fetch and acknowledge changes.

CTX is a tool made to solve a big problem when chatting with LLMs like ChatGPT or Claude: giving them enough context about your project.