mcp server
ai.packmind/mcp-server
Packmind captures, scales, and enforces your organization's technical decisions.
Description as published by the maintainer. Source
- version 1.0.0
- active
active — Most recent push to the repository was 2026-08-06.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 305 | Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. | cumulative, all time | GitHub | |
| Last commit | 2026-08-06 | Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. | point in time | GitHub | |
| Open issues | 47 | Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. | as of fetch | GitHub | |
| Latest published version | 1.0.0 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2025-11-24 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| License | Apache-2.0 | Licence GitHub detected in the repository. Detection can be wrong; the LICENSE file is authoritative. | as of fetch | GitHub | |
| First listed in the MCP Registry | 2025-11-24 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | active | The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. | as of fetch | GitHub |
Where to get it
Related, by what their authors tagged them
-
LiveCheck AI
— last commit 2026-07-30, shares ai-code-governance
Secure MCP runtime server for scanning and autofixing code issues
-
Aperion Shield
— last commit 2026-08-04, shares ai-guardrails
Local guardrail proxy that blocks destructive MCP tool calls, rug pulls, and tool poisoning
-
Forge Engine
— last commit 2026-07-30, shares ai-coding-assistant
Design spec + milestones AI coding agents read before building; drift flagged, changes reviewed.
-
Rampify
— last commit 2026-06-23, shares ai-coding-assistant
SEO MCP server: crawl your site, find AI-visibility gaps, and ship the fix from your coding agent.
-
io.github.atef-ataya/depwire
— last commit 2026-07-21, shares ai-coding-assistant
Dependency graph + 23 MCP tools. Impact analysis, simulation, security, agent coordination
-
CometChat Docs
— last commit 2026-07-20, shares ai-coding-assistant
CometChat docs search + implementation bundles: add chat, voice, video & moderation to your app.
-
io.github.DanielGuru/repomemory
— last commit 2026-04-15, shares context-engineering, copilot
Persistent memory for AI coding agents. Hybrid search, auto-session capture, context routing.
-
Fodda Brand Intelligence
— last commit 2026-08-06, shares copilot
Brand health & trend footprint across PSFK expert graphs with citable sources, not web summaries.
-
Fodda Deep Research
— last commit 2026-08-06, shares copilot
Autonomous deep research reports merging PSFK trend graphs with citable sources.
-
Fodda Earnings Intelligence
— last commit 2026-08-06, shares copilot
Cross-company earnings trends & executive divergence with citable sources.
These share tags the maintainers applied themselves, such as ai-code-governance, ai-guardrails, ai-coding-assistant, context-engineering. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.
This is not a recommendation and not a test result. It is a map of what the authors said their work is about.
How the author describes it
Topics the maintainer set on GitHub: ai-code-governance, ai-coding-assistant, ai-guardrails, claude-code, context-engineering, contextops, copilot, cursor.
Bring your own setup
We take apart real AI setups every week and show what broke, what cost too much, and what the trace actually said. If you run agents on real work, that is where the useful conversation is.
Join ZBS AI Practice Lab