"Information about SQLite database" 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.

Search and discover AI agents, skills, prompts, bundles and MCP connectors from a curated catalog of 4500+ assets. Provides tools for searching, browsing categories, and accessing detailed information about each asset.

Provides AI assistants with real-time access to the AGNO framework documentation by enabling them to browse, search, and fetch documentation pages. This server allows users to query information about AGNO's Agents, Teams, and Workflows directly through MCP-compatible clients.

Enables Claude Code and Gemini CLI to see your macOS screen, answering questions about the file, error, UI, database table, or web page you're looking at. Captures screenshots, reads database schemas, and controls Chrome/Brave, presenting answers as on-screen highlights or spoken bubbles above your cursor.

A persistent memory implementation using a local knowledge graph that lets Claude remember information about users across conversations.

MCP Server for OceanBase database and its tools

The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.

A Model Context Protocol server that enables large language models to interact with Apache Superset databases through REST API, supporting database queries, table lookups, field information retrieval, and SQL execution.

An implementation for managing Neo4j graph database operations through the Model Context Protocol, enabling users to execute Cypher queries against their Neo4j database via AI assistants like Cursor and Claude Desktop.

Agentic tool that looks for statistical variations in conversation structure and logs unusual events to a SQLite database. Built using the Model Context Protocol (MCP), this system is designed to be used with Claude Desktop or other MCP-compatible clients.

Provides an append-only SQLite journal for orchestrator/worker AI workflows, enabling delegation, reporting, verification, and tracking of tasks with built-in search and optional cloud sync.

MADE coordinates auditable human-agent procedures with work claims, review gates, approvals, and resumable sessions. Its local MCP server uses SQLite.

Enables AI clients to programmatically build, validate, run, and test executable agent graphs as nodes and wires through local MCP tools, with SQLite storage and no cloud dependency.

Enables autonomous hierarchical multi-agent orchestration for MCP, with persistent SQLite state, real-time web monitoring, and support for mixing multiple CLI and direct API execution providers.

A transport-agnostic MCP seam for messages, context sharing, and task hand-off between humans, chat bots, and coding agents. Supports multiple backends like SQLite, Redis, Matrix, NATS, and XMPP to bridge Claude chat with Claude Code.

A server that enables AI models to access user geolocation information via EdgeOne Pages Functions through the Model Context Protocol.

Local-first MCP server that enables multiple Claude agents to coordinate through a shared message bus with SQLite persistence and real-time clock anchoring.

Lets multiple agent clients running on one machine or across a LAN register identities, designate one as master and the rest as subagents, and coordinate work through task dispatch, claim, progress reporting, messaging, rooms, and a shared versioned blackboard backed by SQLite. Supports both stdio with a shared database and Streamable HTTP so many clients can connect to a single hub.

Enables agents to inspect Codex quota status, create and preflight tasks, save checkpoints, resume or complete work, and generate deadline-aware schedules through seven MCP tools backed by a local SQLite database.

Enables LLMs to orchestrate AI agents and tasks, inspect DAG workflows, monitor cluster metrics, and perform safe database queries and statements through standardized MCP tools.