mcp server
AgentAnycast
P2P runtime for A2A protocol. Encrypted agent communication with zero-config NAT traversal.
Description as published by the maintainer. Source
- version 0.3.2
- slowing
slowing — Most recent push to the repository was 2026-04-04.
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 | 2026-04-04 | 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 | 15 | 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 | 0.3.2 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-03-19 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| First listed in the MCP Registry | 2026-03-19 | 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
-
io.github.Aidress-ai/aidress
— last commit 2026-08-06, shares a2a, a2a-protocol
Verify, discover, and rate AI agents before transacting.
-
ThinkNEO Control Plane
— last commit 2026-07-06, shares a2a-protocol
Enterprise AI Control Plane: governance, guardrails, spend tracking, compliance & smart routing.
-
Myagi - Open Agent Registry
— last commit 2026-08-03, shares a2a-protocol
Score any website's AI-agent readiness. Open Agent Registry scanner + platform tools.
-
dev.yafl/mcp
— last commit 2026-08-01, shares end-to-end-encryption
Agent-first E2EE file transfer over MCP: move files between machines in one tool call, gone in 24h.
-
io.github.47-ronn/remote-agents
— last commit 2026-07-22, shares end-to-end-encryption
Control fleets of remote machines via AI agents: exec, files, git, fleet ops, map/reduce.
-
io.github.anp2dev/anp2-mcp-server
— last commit 2026-07-08, shares a2a-protocol
AI-agent economic protocol over MCP — identity, reputation, tasks, credit, Sybil resistance
-
io.github.bitatlas-group/bitatlas
— last commit 2026-08-06, shares end-to-end-encryption
Zero-knowledge encrypted storage for humans and AI agents. Client-side AES-256-GCM, EU-hosted.
-
io.github.bobybarack/sovseal-memory
— last commit 2026-08-05, shares end-to-end-encryption
Local-first, zero-knowledge AI memory across Claude, Cursor, and every MCP client. 0 RTT reads.
-
Jitsu
— last commit 2026-08-06, shares golang
Manage Jitsu data pipelines: destinations, streams, connections, functions, live events.
-
Draugr
— last commit 2026-08-06, shares golang
Security scanning for AI agents: SAST, SCA, secrets, IaC, DAST, ranked by real risk.
These share tags the maintainers applied themselves, such as a2a, a2a-protocol, end-to-end-encryption, golang. 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: a2a, a2a-protocol, end-to-end-encryption, golang, grpc, libp2p, mcp, nat-traversal, noise-protocol, p2p, sidecar.
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