"Kit" matching MCP servers:

A provider-neutral MCP server that provides tools for managing skills, agents, references, and artifact memory in a local workspace.

Hosts three live read-only MCP servers enabling agents to retrieve and cross-reference curated fictional and real archives with provenance and source URLs.

Enables creation of persistent, compounding knowledge bases using Karpathy's LLM Wiki pattern with LLM-maintained markdown wikis. Supports automated ingestion, cross-referencing, synthesis, and linting of sources as an alternative to traditional RAG systems.

A production-ready foundation for building secure, observable MCP servers with built-in authentication, rate limiting, and reference tools like database-query and semantic-search.

SQL Server MCP server with AST-based query validation, read-only safety, schema exploration, ER diagram generation, and DBA toolkit integration (First Responder Kit, DarlingData, sp_WhoIsActive).

Enables interaction with Slack workspaces through comprehensive channel management, messaging, user management, file uploads, and Block Kit formatting. Features secure credential storage via macOS Keychain and supports all major Slack operations including reactions and workspace information.

Unified MCP server combining dev, graph, docs, and kit tools for Lamatic.ai. Enables managing orgs/projects/flows, executing flows, querying docs via RAG, and browsing AgentKit contributions.

A starter kit for building Model Context Protocol servers that enables AI tools to access external data and functionalities like checking holidays, disk space, timezones, RSS feeds, code diffs, and web performance metrics.

Enables LLM clients to capture, search, and recall personal and family knowledge as structured facts and narrative memories, with every memory linked to its source.

MCP server for RSS aggregation and LLM summarization, allowing users to fetch latest articles, search archives, and generate topic-specific digests via natural language.

An AI conversation management layer that enables creating chat sessions, persisting message history to GitHub, and performing semantic searches over past interactions. It supports multi-turn threading and context injection to integrate external memory sources into Claude conversations.

A composable semantic memory layer that provides cross-project recall and session context using Qdrant and OpenAI embeddings. It enables users to securely store, search, and manage persistent memories with built-in secret scrubbing for privacy.

A Model Context Protocol server that generates prompts based on Git repository content, including a command to generate PR descriptions from diffs.

A custom Model Context Protocol implementation that integrates Perplexity AI with Claude Desktop, allowing users to access Perplexity's AI models for both single questions and multi-turn conversations.

Transforms any GitHub repository into a documentation hub for AI assistants, enabling access to up-to-date documentation and code to eliminate hallucinations. Supports repository-specific and dynamic endpoints with smart search capabilities.

Fetch web pages into clean, RAG-ready Markdown with a verifiable provenance record — source URL, UTC fetch time, SHA-256 of the response body, redirect chain, and an extraction-fidelity metric. Writes WARC archives and honours robots.txt/ai.txt, so every document an agent reads can be re-verified and cited.

Transforms any GitHub repository or GitHub Pages site into a documentation hub for AI assistants using the Model Context Protocol. It allows AI tools to access real-time code and documentation to prevent hallucinations and provide accurate API usage examples.

Transforms scattered customer feedback from sources like Slack, Zoom, and JIRA into actionable product insights and AI-generated PRDs. It features over 50 tools for semantic clustering, sentiment analysis, and VOC-based prioritization to streamline product management workflows.

Enables agents to interact with Google NotebookLM — ask questions, manage notebooks and sources, generate audio overviews, and extract citations — via a stealth Chrome automation layer.

Persistent memory for AI agents — organized by time and space. Important memories get promoted, noise decays naturally, and related knowledge clusters into a browsable topic tree. Fully automatic.