mcp server
Reasoning Commons
AI Reasoning Cache & Consensus Layer with 11 MCP tools via Streamable HTTP.
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 | 0 | 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 | 2 | 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 | 2026-05-23 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| First listed in the MCP Registry | 2026-05-23 | 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
-
AI Reasoning Commons
— last commit 2026-08-06, shares agent-memory, ai-debugging, coding-agents
Search, reuse, verify AI reasoning. Task marketplace with leaderboard. Zero-barrier, no auth.
-
PWA Debug Layer
— last commit 2026-06-26, shares ai-debugging
Debug PWAs in your real browser via MCP: service-worker, cache, installability & framework state.
-
io.github.bromoket/x64dbg
— last commit 2026-06-08, shares ai-debugging
MCP server for x64dbg debugger - 23 mega-tools for AI-powered reverse engineering and debugging
-
io.github.alibaizhanov/mengram
— last commit 2026-07-30, shares agent-memory, cursor-ai
Long-term memory for AI agents: semantic facts, episodic events, and procedural workflows
-
io.github.1111111111111111111114oLvT2/inquisitor
— last commit 2026-08-05, shares debugging, opencode
Hypothesis-driven problem solving for AI agents: probe, falsify, escalate.
-
DebugBundle
— last commit 2026-07-29, shares debugging
DebugBundle MCP: runtime error reporting, incident response, health checks, and product analytics.
-
com.gitscrum/mcp
— last commit 2026-02-12, shares cursor-ai
MCP server for GitScrum — manage tasks, sprints, time tracking, and client workflows via AI
-
Zapier
— last commit 2026-07-29, shares cursor-ai
Hosted MCP server connecting AI assistants to 9,000+ apps and 40,000+ actions via Zapier.
-
LinkedIn MCP Server (Salesbot)
— last commit 2026-07-27, shares cursor-ai
Human-in-the-loop LinkedIn outreach and a built-in sales CRM for AI agents. Safety-gated, anti-spam.
-
deadends.dev
— last commit 2026-08-06, shares debugging
Structured failure knowledge for AI agents — dead ends, workarounds, error chains
These share tags the maintainers applied themselves, such as agent-memory, ai-debugging, coding-agents, cursor-ai. 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: agent-memory, ai-agent, ai-agents, ai-debugging, claude-code, coding-agents, cursor-ai, debugging, developer-tools, failure-analysis, failure-memory, llm-tools, mcp, mcp-server, mcp-tools, model-context-protocol, opencode, reasoning-cache, windsurf.
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