Spec Driven Development
by formulahendry
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Runs on your computer★ 440MITUpdated 14 months ago
Install
Preloop
Overview
Description from the maker (original English)
Open-source AI agent control plane and software factory: govern every tool and model call.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiNot measured yet★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs in the cloud
- Transport: Streamable HTTP
- Category: Other
- You need to fill in: Authorization
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on the maker’s server: what you ask it goes through their server.
- You fill in yourself:
Authorization
Values marked secret are keys to your account. Never share them or paste them into a chat.
You need
- Runs in the cloudNothing to install; it connects over the internet
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
This address needs a key in a request header, which the add-connector menu cannot set. Use Claude Code, Cursor or VS Code instead.
Run this in your terminal
claude mcp add --transport http preloop https://preloop.ai/mcp/v1 --header 'Authorization: your-value'Fill in the empty values before running it.
Or copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"preloop": {
"url": "https://preloop.ai/mcp/v1",
"headers": {
"Authorization": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"preloop": {
"type": "http",
"url": "https://preloop.ai/mcp/v1",
"headers": {
"Authorization": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This address needs a key in a request header, which the add-connector menu cannot set. Use Claude Code, Cursor or VS Code instead.
Your choice stays on this device in a cookie; no account needed.
Values you fill in
Authorizationsecret HTTP headerOptional. Preloop access token as 'Bearer ' (from 'preloop auth token'). OAuth-capable clients can omit it.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
We have not read this server’s tool list yet; we check endpoints in rotation.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account preloop, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- … Not checked yet
Endpoint responds
Not checked yet; we check endpoints in rotation
- … Not checked yet
Sign-in with OAuth
Not checked yet; we check endpoints in rotation
- … Not checked yet
No hidden instructions in tool descriptions
Not checked yet; we read the tool list when we check the endpoint
Compared with others in Otheri
- More stars than 0% of the other 32398 servers in this category
- Published to the registry more recently than 74% of the other 32398 servers in this category
- Basic checks equal to or better than 100% of the other 32398 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Preloop safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
You need to fill in: Authorization
Does it run on my computer or in the cloud?
It runs in the maker’s cloud. Connect over the internet with nothing to install, but what you send passes through the maker’s servers.
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.17.1) reached the registry 13 days ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.
What can I use instead?
In the same category: Spec Driven Development, Remote Desktop Commander, AI Deprecations
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
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by desktop-commander
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Runs in the cloud★ 211Updated yesterday
Install
AI Deprecations
by aideprecations.com
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Runs in the cloudUpdated today
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README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
Preloop
The open-source AI agent control plane and software factory.
Preloop is one self-hostable service that sits between your AI agents and everything they reach, and runs agents for you when work arrives. Every tool call and every model call is governed, attributed and visible, whether a person started the agent or a ticket did. You see gateway spend by model, agent, session, API key and flow, and cap it with budgets. Imported Copilot usage is displayed separately as vendor-reported data.
It has two halves, and the same policies, budgets and session timeline apply to both:
- Control plane. An MCP firewall that allows, denies or holds any tool call for human approval; an AI model gateway with budgets and attribution; policy-as-code in YAML and CEL; one runtime timeline per session.
- Software factory. Flows run agents, in hosted containers or on your own runners, when GitHub, GitLab, Bitbucket Cloud, Jira, a webhook or a CI job fires. Presets review pull requests, implement issues, triage tickets, scan for vulnerabilities and collect evidence packs. A human can steer a running agent from the console, phone or watch, and the work lands as pull requests you can review.
Both halves are multi-user: invite colleagues and organize teams in Preloop (OSS). Each user can connect devices, runners and agents. Preloop (OSS) has no roles or access controls between users of an account; those are in Preloop Cloud and Preloop Enterprise.
Who it is for:
- Platform and security teams that need agents governed before they reach production systems.
- Engineering teams running coding agents that want to see the cost and keep an audit trail.
- Teams that want tickets and events turned into bounded agent work they can review.
MCP clients connect to /mcp; existing /mcp/v1 configurations remain compatible. See the tested MCP protocol matrix for modern discovery, legacy initialization, and SDK migration details.
Works with OpenClaw, Claude Code, Codex CLI, Cursor, Gemini CLI, Hermes, OpenCode, Devin, Copilot CLI, and any MCP-compatible agent.
Your first five minutes
# 1. Install the CLI (macOS / Linux / WSL)curl -fsSL https://preloop.ai/install/cli | sh
Windows, native (PowerShell): irm https://preloop.ai/install/cli.ps1 | iex
Details: docs/windows-cli.md
2. Connect it to a control plane
preloop signup # Preloop Cloud (fastest), or preloop login --url http://localhost:3000 # your self-hosted instance
3. Bring local agents under governance
preloop agents discover
preloop agents discover finds local agent configs and reports what each adapter supports. preloop agents onboard configures them: calls routed through the managed MCP entry reach the MCP Firewall, supported model routes use the Gateway. To talk to a running agent, install the runtime plugin with preloop agents install-plugin (or preloop claude for Claude Code). Full walkthrough: onboard local agents.
Watch it work
Onboarding, the MCP firewall, human approvals, and cutting session cost. Recorded against a real stack, no slideware.
Watch the full playlist →
What you get
Jobs teams otherwise buy from several vendors, in one Apache 2.0 stack:
| Capability | What it does | Alternatives |
|---|---|---|
| MCP Firewall | Govern every tool call. Allow, deny, require approval, require justification. YAML + CEL. Connect OAuth MCP servers with automatic token refresh. | MintMCP, Lunar.dev MCPX, TrueFoundry |
| AI Model Gateway | OpenAI-, Anthropic- and Gemini-compatible. Budgets, allowed-model lists, token accounting, attribution. | Portkey, Helicone, LiteLLM, Kong AI |
| Flows | Start an agent when a tracker, webhook, or CI job fires, with the same firewall, approvals, and cost. preloop flow trigger. | Custom CI glue, AgentCore Runtime |
| Private runners | Run flow agents on your own machines over an outbound WebSocket, no inbound ports. | Self-managed CI runners |
| Agent Control | Talk to long-running agents from the console, phone, or watch. Steer runs with operator notes. | Terminal access |
| Cost & Budgets | Spend by model, agent, session, API key, flow, and user, including imported Copilot usage. | FinOps dashboards, vendor billing exports |
| Human Approvals | Mobile, watch, Slack, Mattermost, email, webhook, or preloop approvals. Native Bash/Edit. Agents can ask_user. | Custom Slack bots, Peta Desk |
| Runtime Observability | One session timeline: tool calls, model calls, policy, approvals, spend, outcomes, and synchronized recordings. | AgentOps, Langfuse, LangSmith |
| Fleet Map | People, devices, agents, models and observed work in one map. | Spreadsheets |
| Evidence packs | Apache flow presets write result.json plus an evidence directory for CRA / AI Act-style work. Not a certification. | Custom GRC folders |
AI Agent → Preloop → [Policy] → Allow / Deny / Require Approval → Execute→ [Gateway] → Budget + attribution → Model
The software factory
One loop, end to end: an issue event arrives from GitHub, GitLab, Bitbucket Cloud or Jira. A flow starts an agent in a hosted container or on your own runner. Every tool call it makes passes the same firewall and approvals as your own agents, and every model call is charged to the flow's budget. The agent opens a pull request; the Pull Request Reviewer reviews it; review or CI feedback resumes the implementation on the same branch, and the result is a pull request you can review. You can steer the run with an operator note at any point, and the whole run is one timeline.
21 presets ship in this repository, including:
- Issue Triage Assistant and issue refinement
- Pull Request Reviewer
- Delivery Report: a weekly report with proposals for human decisions and project reruns. See what landed, what the available evidence supports against stated criteria, and which gaps deserve investigation
- Automated issue implementation
- Security audit presets: SBOM verify, exploit check, release audit. They collect machine evidence for CRA- and EU AI Act-style reviews. That is not a conformity assessment, certification, or legal advice.
- Observe / Eval
- Component due diligence, architecture and strategy review, repo code health review
- Standards compliance walk, release backport, weekly model price review
Or write your own. Trigger from CI with the run-flow GitHub Action; run agents on private runners; let a flow start child flows with flow delegation. Tracker setup: Bitbucket and Jira quickstart; Bitbucket Data Center is available as an opt-in provider.
Policy-as-code
version: "1.0"metadata: name: "Production Safeguards"
approval_workflows:
- name: "deploy-approval" timeout_seconds: 600 required_approvals: 1 async_approval: true
tools:
- name: "bash" source: mcp approval_workflow: "deploy-approval" justification: required conditions:
- expression: "args.command.contains('deploy') && args.command.contains('production')" action: require_approval
Check a sample call before saving with Test this rule or Simulate in the console. Ship it with preloop policy apply (validate / diff / export also exist).
More capabilities
- Model providers: OpenAI, Anthropic, Google, Amazon Bedrock, Azure OpenAI, Alibaba Cloud Model Studio, DeepSeek, Mistral, Moonshot, Z.ai, OpenRouter, any OpenAI-compatible endpoint, and runner-proxied local models. Reviewed model pricing; unpriced models stay visibly unpriced.
- Restricted CI identity: a machine identity bound to one project and one flow for CI jobs, with an optional read-only native GitHub App clone mode and separate result publication. Guide.
- Account kill switch: blocks gateway and tool traffic and stops managed flow runs, with audited recovery. Guide.
- Durable audit delivery: opt-in transactional intents for approvals, policy decisions and tool outcomes, with restart replay and backlog alerts. Operations guide.
- Session search: keyword search by default, semantic as an opt-in, every search audited. Saved searches.
- DORA exports: the AI-agent slice of the ICT inventory and incident record. Guide.
- OTLP export: off by default; emits OpenTelemetry GenAI spans. Guide.
- Chat: Slack, Mattermost, and Discord for questions, approval votes and operator notes. Chat connections.
- Event-driven employees: managed agents doing bounded tasks from tracker and error events. Guide.
- Remote sessions and deployment: [remote sessions](docs/guide/runners/remo
Only the beginning is shown; read the rest in the repo