Server data from the Official MCP Registry
Backend for AI agents: one batched tool over ~668 actions (hosting, storage, auth, payments).
About
Backend for AI agents: one batched tool over ~668 actions (hosting, storage, auth, payments).
Remote endpoints: streamable-http: https://agentstack.tech/mcp
Security Report
Valid MCP server (1 strong, 0 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry.
1 tool verified · Open access · No issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Found in Source Code
Found by scanning the linked source code. This listing connects to a hosted endpoint, so none of this runs on your machine: it describes what the server software does where it is hosted.
How to Connect
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-github-agentstacktech-agentstack": {
"url": "https://agentstack.tech/mcp"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
AgentStack
After one bind, the agent does not shop the catalog. Work Graph hands it the next step. Live edits stay on the safe circle: write a copy, pass the checks, then publish.
One MCP tool, agentstack.execute. The server holds the plan. Each turn is one action. Idle is a valid answer. Blocked names the unblock.
Story: agentstack.tech/work-graph · Contract: docs/plugins/WORK_GRAPH.md · Safe circle: agentstack.tech/mcp-docs#safe-cycle
The loop
session → context.project_id → agents.work_next → packet.next_action → claim / execute → work_next
Recipe mcp_work_loop_v1. Prompt agentstack_closed_loop_autonomy.
flowchart LR
S[Session] --> B[Bind project]
B --> W[work_next]
W --> N[next_action]
N --> X[Claim and execute]
X --> W
When that step changes live data, the same project runs the safe circle:
connect → sandbox → review → promote
The agent writes a generation. Customers stay on the previous copy until the checks pass. The project’s own rule publishes it: at once, or in stages.
| Who | Without the loop | With Work Graph |
|---|---|---|
| Founder with open loops | Every new chat re-reads the tool menu | The outcome is written once. The next chat takes the head of the queue |
| A person with more tasks than hours | The agent invents a tool and spends the window choosing | The list says ready, blocked, or idle |
| A business that needs the work finished | Thrash between catalog, guess, and retry | Claim, execute, verify, on the same project as the site and the payments |
| Someone wiring an agent | Paste the catalog into the prompt | agents.work_next, then packet.next_action |
Measured on 2026-10-07
Local registry build. Not a production traffic sample. Not a provider tokenizer. Bytes ÷ 4 is an English size estimate.
| Payload | UTF-8 bytes | Rough tokens |
|---|---|---|
Example next_action plus two alternatives | 250 | ~63 |
discovery.search, first 20 compact rows | 8,889 | ~2,222 |
| Handshake contract JSON, once | 11,297 | ~2,824 |
| Compact search, 50 rows (HTTP max) | 20,852 | ~5,213 |
| Full registry catalog JSON (795 actions in that build) | 1,680,071 | ~420,018 |
The full catalog is 6,720× that example packet. A 20-row search page is 35.6× it. The public catalog you should quote is 714 actions — live GET /mcp/actions/summary. Do not add the byte ratio and the operator targets into one savings claim.
| Signal | Target |
|---|---|
Autonomous loops that follow packet.next_action | ≥ 90% |
| Catalog shopping while a packet is already present | ≤ 5% |
Those are steady-production goals, not a live counter. Method and scenarios: docs/plugins/WORK_GRAPH.md.
Docs
This repository is the public documentation (English, user- and integrator-facing): web product, MCP, plugins, REST, RAG, sandboxes, and examples.
If you use the website: docs/USER_FEATURES_GUIDE.md.
If you build a product: docs/BUILD_YOUR_PRODUCT.md — @agentstack/sdk, optional genetic-ai-starter.
If you run agents on a large codebase: docs/genetic-system/.
If you integrate: docs/MCP_AND_ECOSYSTEM.md — start with the loop, then the catalog (714 actions · docs/MCP_SCALE.md).
REST / OpenAPI: docs/OPENAPI.md — Swagger · openapi.json.
npm SDK: @agentstack/sdk · docs/sdk/
Full index: docs/README.md · What's new: docs/WHATS_NEW.md
Open source kit
| Repo | Branch | Notes |
|---|---|---|
| genetic-ai-starter | main | Map-first install: npx @agentstack/genetic-ai-starter init. SoT lives in this repo under genetic-ai-starter/ when you have the monorepo. |
Plugins
| Plugin | Notes |
|---|---|
| cursor-plugin | v0.4.18, gen3 — rules, skills, commands, agents, hooks; Plugin MCP Connect and OAuth 2.1 device code |
| claude-plugin | Claude Desktop / API installers |
| gpt-plugin | ChatGPT / OpenAI ecosystem |
| vscode-plugin | VS Code marketplace distribution |
Product
- Site: agentstack.tech
- Work Graph: agentstack.tech/work-graph
- GitHub org: github.com/agentstacktech
Reviews
No reviews yet
Be the first to review this server!
More Developer Tools MCP Servers
Git
Freeby Modelcontextprotocol · Developer Tools
Read, search, and manipulate Git repositories programmatically
Fetch
Freeby Modelcontextprotocol · Developer Tools
Web content fetching and conversion for efficient LLM usage
Worldmonitor
Freeby Koala73 · Developer Tools
Live markets, conflicts, country risk, chokepoints, energy, and China decision signals. 89 tools.
Paperclip
Freeby Paperclipai · Developer Tools
Trending hip-hop artist momentum scores across four cultural dimensions.
Toleno
Freeby Toleno · Developer Tools
Toleno Network MCP Server — Manage your Toleno mining account with Claude AI using natural language.
mcp-creator-python
Freeby mcp-marketplace · Developer Tools
Create, build, and publish Python MCP servers to PyPI — conversationally.