"Spec-WorkFlow documentation or implementation" matching MCP servers:

Enables AI assistants to use Neo4j knowledge graphs and Qdrant vector databases for hybrid reasoning, combining structured facts with semantic search for advanced knowledge management, research analysis, and standardized coding workflows.

Repository-native, Git-reviewable memory extension for AI coding assistants to persist project context across features, using SQLite caching for up to 10x token reduction.

A Model Context Protocol implementation that enables AI assistants to interact with markdown documentation files, providing capabilities for document management, metadata handling, search, and documentation health analysis.

Enables users to access, search, and get recommendations from AWS documentation through natural language queries. Supports both global AWS documentation and AWS China documentation with tools to fetch pages, search content, and discover related resources.

Eleven local stdio MCP servers exposing a reproducible EPA SWMM stormwater-modelling workflow: model building, simulation with run manifests and continuity checks, calibration, GIS/QGIS, design storms and climate scenarios, uncertainty analysis, plotting, and modelling memory.

Provides a shared project knowledge and handoff layer for coding agents, offering tools to list, search, retrieve, and update stages of project information to maintain continuity across long-running projects

Enables AI agents to export, import, validate, and inspect memories in the Memory Interchange Format (MIF), ensuring portability across different memory systems.

An open-source MCP server that provides applications like Cursor, Windsurf, and Claude with access to llms.txt documentation files, allowing users to control and audit context retrieval.

Enables AI agents to read, search, and preview governed markdown memories with explicit state and sensitivity, plus quarantine-only write, while never mutating canonical memory or exposing undeclared content.

Provides AI agents with a secure offline-first long-term memory system using Obsidian Markdown vault and 3D knowledge graph, with RLM-based querying to avoid context overload.

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.

An improved implementation of persistent memory using a local knowledge graph with a customizable --memory-path. This lets Claude remember information about the user across chats.

Search firsthand observations that agents recorded while doing real work — what actually happened with a product, API, service, or place, rather than what its documentation claims. Agents can also write back what they observed.

Auto-captures decision context from multi-agent workflows to preserve the 'why' behind every choice. Enables task traceability, reasoning retrieval, and continuous improvement across planning and implementation sessions.

An MCP server implementation that enables interaction with the Letta API for managing agents, memory blocks, and tools in the Letta system.

AI workflow orchestration platform with 100+ integrations, knowledge graph, and unified credits. Build, manage, and execute workflows from Claude Code, Cursor, or any MCP client.

Local-first MCP server that gives any AI coding agent per-project memory, workflow intelligence, and always-on, lossless token & context optimization.

An MCP server implementation that helps Claude remember and learn from programming errors by storing, retrieving, and finding similar errors with full context.

Enables access to Yuque (语雀) documentation through cookie-based authentication, supporting document retrieval, search, TOC navigation, and content management from knowledge bases via URL or namespace.

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