"Information about project memory" matching MCP servers:

Bridges disconnected AI sessions with persistent project memory and automatic session handoff, integrating GitHub workflows for context-aware development.

Enables AI assistants to discover and interact with GitHub repositories through standardized MCP tools, allowing exploration and retrieval of project information without direct API calls.

Provides unified project context and monitoring capabilities including project health metrics, build diagnostics, Git integration, and infrastructure validation to initialize development sessions with comprehensive project information.

Enables querying any GitHub, GitLab, or Bitbucket repository and getting AI-generated answers about the codebase, powered by Gemini.

Enables asking natural-language questions about any GitHub repository and receiving answers grounded in the actual code, with read-only enforcement and automatic repository fetching.

Semantic git queries via MCP. Beyond git log — answer who/what/why about any line, file, or branch with blame, co-change, PR linkage.

Enables natural-language queries about public GitHub repositories, including issues, pull requests, repo metadata, and READMEs, via the GitHub API.

Provides fresh project context to coding agents by combining Markdown documentation and live Git state, enabling deterministic startup briefs and bounded document retrieval for MCP-compatible tools.

An MCP server that reads public GitHub Projects v2 boards (including item-level data like status and custom fields) without authentication, using official APIs and fallback methods for user-owned projects.

A Model Context Protocol server that enables AI assistants to interact with Azure DevOps services, providing capabilities for work item management, project management, and team collaboration through natural language.

Fetches glasz-style spec files (product.md, tech.md, test-plan.md, and INDEX.md) from GitHub repositories, enabling LLMs to answer questions about features, stories, and acceptance criteria without cloning or pasting files.

An MCP (Model Context Protocol) server for handling GitHub URLs with validation and parsing capabilities. This server provides tools to convert between GitHub repository information and URLs, with intelligent validation to distinguish between public repositories, private repositories, and non-exist

Analyzes GitLab group projects to extract purpose, I/O, dependencies, and database tables, serving this knowledge via MCP tools and a read-only HTML visualization.

Repository-native protocol and MCP server for coordinating work items, documentation, changelogs, and project memory between humans and AI agents, using Markdown files in a Git repository as the canonical data source.

An MCP server that gives AI agents structured read/write access to a story-based project backlog. Agents can list stories, read content, update status, and append notes — all backed by plain markdown files that live inside your project repository. There is no shared server. The backlog files live in your repo under requirements/, committed and versioned alongside your code

An MCP server that caches your GitHub starred repositories in a local SQLite database and lets you search, list, and retrieve details about them through any MCP client (Claude Desktop, Cursor, VS Code, etc.), with automatic freshness checks via GitHub's ETag mechanism.

Open-source team memory layer for AI coding agents. Markdown-in-git, user/team/org hierarchy, MCP. Apache-2.0.

Persistent memory for AI coding agents. Builds semantic memory from git history and codebase, searchable via MCP tools.

MCP server integrating Google Search Console, Google Analytics 4, and GitHub, enabling AI clients to query search analytics, traffic data, and repository information.

Enables AI agents to interact with Microsoft TFS / Azure DevOps Server work items, Git repository files, and identity lookup through the TFS REST API, including bulk operations and cross-project access.