"Using Logseq data as context for chat applications" matching MCP servers:

A Model Context Protocol server that enables AI agents to interact with local Logseq knowledge graphs, supporting operations like creating/editing pages and blocks, searching content, and managing journal entries.

Exposes a running DeepSeek Harness as an MCP endpoint so any other agent or script can create workspaces and sessions, hand coding tasks to the DSH agent, read and write files, run shell commands, and invoke permitted harness tools under the same sandbox, transcript, and permission policy as the DSH UI.

Enables dispatching entire autonomous multi-agent engineering swarms directly from IDEs like Cursor and Windsurf via a single MCP endpoint, with streaming progress updates and verified git diffs returned to the editor.

Provides a local chat room for coordinating work across Git worktrees, coding agents (Claude, Codex), and humans, with tools to start, wake, stop, message, and search agent sessions.

Enables remote control of the Cursor AI agent via chat platforms like GitHub Issues or Telegram, allowing users to start sessions and receive summaries and prompts in a chat thread.

Lets any MCP host or OpenAI-compatible client chat with Meta's Muse (muse.ai Hatch) agent by driving your own logged-in Chrome, exposing tools for status, login, new chat, prompting, and reading replies. It also serves an OpenAI-compatible HTTP endpoint with real SSE streaming and prompt-injected tool calling, plus a small CLI for one-shot generation.

Enables remote chat between Feishu (Lark) and AI agents like Cursor/Claude via MCP, allowing users to send messages from Feishu groups and receive streaming replies.

Wraps Tool and ToolPackage instances from the llm-chat ecosystem behind the Model Context Protocol, enabling stdio and Streamable HTTP transports for MCP servers.

Exposes local stdio MCP servers to chat AIs through temporary public URLs, providing both a Streamable HTTP gateway and a REST+OpenAPI bridge for integration with ChatGPT and other clients.

Enables AI coding agents to collaborate on the same project by sharing session briefs and reading each other's native transcripts, memories, and instructions in place, with zero-copy, across different agent tools.

Enables AI agents to send messages to human users via chat platforms (currently Discord) and optionally wait for human responses, facilitating human-in-the-loop interactions.

Enables agents to inspect configured LLM models and send chat requests to them, with support for parameter overrides, file-based prompts, and output to files.

Context window usage estimation for AI coding agents via MCP, enabling proactive state preservation before compaction.

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.

MCP server providing lossless file reads with deduplication, per-repo token metering, and HMAC-signed context receipts. Enables auditable, vendor-neutral measurement of what an AI agent saw.

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.

Aggregator MCP proxy that collapses N downstream MCP servers into 4 meta-tools with progressive tool discovery, and compresses large tool outputs (HTML→Markdown, JSON summarization) with full-output retrieval via read_more and a per-session token-savings report.

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

Enables multiple AI coding agents and humans to coordinate through a Git-native Markdown blackboard (.context/ + AGENTS.md), where they can inspect workspace overviews, claim and toggle ready tasks, record architecture decisions, synthesize shared prompts, and hand off work via atomic, lock-protected writes that prevent lost updates and formatting drift. It also serves a fully offline local web dashboard that live-syncs any blackboard change over WebSocket.

MCP server providing virtual memory for AI context with durable SQLite storage, retrieval, and bounded prompt assembly to manage context windows.