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

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 enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

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

Durable project-memory MCP: decisions, constraints, and pipelines across Claude sessions

Long AI conversations fail in predictable ways. Context-First fixes all four: Failure Mode What Goes Wrong Context-First Solution Context Drift AI forgets earlier decisions and intent as the conversation grows context_loop + detect_drift continuously re-anchor every turn Silent Contradiction New inputs silently overrule established facts — the AI doesn't notice detect_conflicts compares every inp

A high-performance MCP server providing up-to-date documentation for Go, npm, Python, Rust, Docker, Kubernetes, Terraform, and more — fetched from official sources, not training data.

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

Enables MCP-compatible coding agents to share persistent project context, task claims, handoffs, decisions, test reports, and searchable history through a local or team-hosted SQLite-backed service with a React dashboard.

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.

Provides over 1,000 creative ways to decline requests across four categories (polite, humorous, professional, and creative). The MCP server wraps a REST API to help users craft professional rejections through natural language interactions.

A local Model Context Protocol server designed to share contextual information between an AI and a user. It primarily provides a tool to retrieve the current date and time in ISO 8601 format based on the server's local timezone.

MCP server providing 29 A-share analysis skills including real-time data, capital flow, limit-up tracking, technical/fundamental analysis, backtesting, risk control, and Xueqiu portfolio tracking, enabling AI agents to execute market research and strategy tasks.

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

Gives AI agents governed context about a data warehouse — table meaning, ownership, lineage, freshness, and PII flags — plus read-only, row-limited, PII-protected querying across DuckDB, Snowflake, and Databricks. Guardrails enforced at parsing, connection, and result layers keep agents from writing, running away, or leaking personal data.

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.

A persistent memory and context management system for AI CLI tools that utilizes a three-layer architecture and semantic search to prevent context loss between sessions. It provides time-aware orientation and smart memory routing to help AI agents maintain project knowledge and architectural decisions.

An MCP server that provides persistent, cross-session memory and team knowledge sharing for AI development workflows. It enables project DNA scanning, semantic search, context budgeting, and git-aware indexing to prevent AI context loss between sessions.

Enables ChatGPT to search, read, and organize your Google Keep notes through read-only MCP tools, including full note retrieval, label listing, recent-note browsing, and task extraction. Notes refresh automatically from your connected account without any local database or write access.

Allows coding agents to securely retrieve curated, sealed Slack message bundles (capsules) via MCP, with full audit and expiration controls.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

Enables agents to import selected conversation archives, search and edit an Obsidian-compatible Markdown memory wiki, and read/write shared context across AI clients through MCP.

Enables AI agents to use a neuro-symbolic memory fabric with bi-temporal knowledge graph and holographic VSA, providing tools for adding, searching, temporal queries, auditing, and proving memories with cryptographic provenance and zero-LLM ingest.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Self-host a realtime voice-call companion for coding agents. Exposes an MCP endpoint that agents can poll as an alternate input stream.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

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.

MCP server for Context Studio, a file-based CMS for governed marketing documents and searchable collections, enabling retrieval and management of AI context via tools like scopes, document reads, and collection search.

A local MCP server that semantically searches Markdown context files and extension context links, providing tools for context_search, context_read, and context_status.

An MCP server that records agent execution metrics and exposes a Context Window Explorer to visualize exactly what entered the model's context window across sessions, tokens, and tool calls.

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.