"A server providing context 7" matching MCP servers:

Enables persistent, auditable business operations state by storing knowledge, evidence, decisions, tasks, execution receipts, and review results in a local SQLite database, so different sessions can continue the same project.

Enables secure read-only access to Tencent Lexiang knowledge bases via MCP, including listing directories, RAG search, and reading entries.

Read-only MCP access to the p-e relay store: an append-only corpus of provenance-bearing coordination records, with tools to fetch a record by id, list records and replies, and check existence. This image serves a snapshot frozen at build time; append_relay is listed but refused, and deposits to the live store require an HMAC-signed request.

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.

Provides persistent personal context storage across AI conversations, allowing AI assistants to remember user preferences, project conventions, and other personal information between sessions.

MCP server for foldkit that exposes the 7-prime spine, 7 κ-bands, and 6 fold operations as native tools and resources in any MCP client.

Enables MCP clients to search a Zotero library, read items, take notes, organize collections, and perform semantic search over PDFs.

Provides a compiled knowledge substrate, extracting atomic claims from an append-only capture log and querying a bitemporal claim graph over MCP. Read-only by default, with opt-in writes for trusted sources.

MCP server providing context usage estimation, conversation compaction, and durable semantic memory via local embeddings and SQLite.

Local-first knowledge operating system and deterministic issue-to-PR factory for AI coding agents. Features hybrid storage (SQLite+FTS5), 7-mode token-aware context compiler, LifeOS txonomy, and MCP stdio server.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Enables AI agents to securely retrieve pre-compiled bounded context and memory state over a zero-network Unix Domain Socket, ensuring tenant isolation and sub-millisecond rehydration in Kubernetes environments.

Provides context from previous sessions to new OpenCode sessions for continuity in the same project.

Enables AI clients to read and write to a user-owned markdown bucket via S3-compatible storage, providing a persistent shared context layer across different AI tools.

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.

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

Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.

Provides redacted access to a private local knowledgebase for coding agents, allowing them to inspect files while hiding sensitive names and identifiers.

A powerful context management system that maintains persistent context across coding sessions, helping development teams track project structure, dependencies, and progress.

Enables AI agents to interact with an embedded graph database (GrafeoDB) via the Model Context Protocol, providing tools for graph CRUD, GQL queries, full-text and vector search, and graph algorithms.

Indexes codebases cut at function and class boundaries into Oracle AI Database 26ai, letting AI coding tools search code by name or meaning, trace callers, callees and change impact, read per-module wiki pages, and recall facts from earlier sessions. All of it is served over the Model Context Protocol with embeddings computed in-database, so no source code leaves the machine and no API key is required.

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.

An MCP server for managing contextual data as markdown files with metadata, enabling agents to save, retrieve, search, and delete contexts using simple CRUD operations.

Simple, fast, long-term memory for your AI chatbots, agents, and MCP tools. Give your LLM a persistent memory using a local SQLite file. No complex database servers, no heavy cloud setup, and zero C++ compilation errors. Works everywhere.

Free Industry 4.0 / IIoT tools, no API key or account: lint MQTT Sparkplug B topics, check a Unified Namespace (UNS) for naming and hierarchy problems, and run an industrial AI-readiness diagnostic for manufacturing digital transformation and smart-factory projects. Works in Claude Code, Codex and any MCP client.

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.

Enables AI coding agents to persist structured long-term memory (gotchas, architecture, API notes) in a .context folder and sync across devices and agents via Git.

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 preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.