Edgegate MCP

Name: Edgegate MCP
Author: frozo-ai

作者：frozo-ai版本 0.19.0i★ –i在你的电脑上运行

注册名称io.github.frozo-ai/edgegate-mcp

安装

概览

作者提供的说明（英文原文）

Regression-test AI models on real Snapdragon and Jetson hardware, with signed evidence for CI.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 搜索与 AI 记忆
你需要填写：EDGEGATE_API_KEY

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
需要你自己填写： EDGEGATE_API_KEY, EDGEGATE_API_URL

标为机密的值是你账户的钥匙。千万不要分享给别人，也不要粘贴到聊天里。

你需要

npm已安装 Node.js（用于 npx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "edgegate": {
      "command": "npx",
      "args": [
        "-y",
        "edgegate-mcp"
      ],
      "env": {
        "EDGEGATE_API_KEY": "your-value",
        "EDGEGATE_API_URL": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add edgegate --env EDGEGATE_API_KEY=your-value --env EDGEGATE_API_URL=your-value -- npx -y edgegate-mcp

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "edgegate": {
      "command": "npx",
      "args": [
        "-y",
        "edgegate-mcp"
      ],
      "env": {
        "EDGEGATE_API_KEY": "your-value",
        "EDGEGATE_API_URL": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "edgegate": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "edgegate-mcp"
      ],
      "env": {
        "EDGEGATE_API_KEY": "your-value",
        "EDGEGATE_API_URL": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

EDGEGATE_API_KEY 机密必填 env
EdgeGate API key. Generate one at https://edgegate.frozo.ai/workspace/ /settings#api-keys
EDGEGATE_API_URL env
EdgeGate API base URL. Defaults to the hosted API; override only for self-hosted deployments.

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 frozo-ai 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 5668 个服务器的 0%
发布到注册中心的时间比此分类中其他 5668 个服务器的 28% 更新
基础检查结果等于或优于此分类中其他 5668 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Edgegate MCP 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

你需要填写：EDGEGATE_API_KEY

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.19.0）于3个月前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者：upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT昨天更新

安装

Codebase Memory

作者：DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT21天前更新

安装

Exa

作者：exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT2个月前更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

edgegate-mcp

MCP server for EdgeGate — set up edge-AI regression gates on Snapdragon devices directly from Claude Code, Cursor, or Claude Desktop.

What does it do?

EdgeGate runs AI model regression tests on real Snapdragon hardware via Qualcomm AI Hub, then produces signed evidence bundles you can attach to CI gates. This npm package exposes EdgeGate's REST API as 61 MCP tools, plus bundled skills, so you can drive the whole flow from a prompt:

> Use the edgegate MCP to set up a CI gate for my MobileNet ONNX model.

Gates: inference_time_ms ≤ 10, peak_memory_mb ≤ 150.

Devices: Galaxy S24, Galaxy S23.

Install

# 1. Generate an API key in the EdgeGate dashboard

https://edgegate.frozo.ai/workspace/ /settings#api-keys

2. Run the installer from the edgegate-mcp package (writes config for Claude Code / Cursor / Desktop)

npx --package=edgegate-mcp@0.21.2 edgegate-mcp-install

Restart your MCP client. Done.

Use it on the web (claude.ai, ChatGPT)

No install and no API key. Add the hosted endpoint as a custom connector and approve it in your browser:

https://edgegate.frozo.ai/mcp

Claude — Settings → Connectors → Add custom connector.
ChatGPT — Settings → Apps & Connectors → enable Developer mode, then create a connector with OAuth authentication.

You sign in to EdgeGate and approve once; disconnect any time under Settings → Connected AI apps in the dashboard. The hosted endpoint has no access to your filesystem, so edgegate_export_run_report returns the report inline instead of writing a file.

Any client that speaks Streamable HTTP can also use it with an API key as a bearer token:

claude mcp add --transport http edgegate https://edgegate.frozo.ai/mcp --header "Authorization: Bearer egk_live_..."

Self-hosting: handleMcpRequest from edgegate-mcp/http is a Web-standard (Request) => Response handler you can mount on any fetch runtime.

Manual config

If you'd rather edit config files yourself, the server is a standard stdio MCP. Add this to your client's config:

Claude Code (`~/.claude.json`)

"mcpServers": { "edgegate": { "type": "stdio", "command": "npx", "args": ["-y", "edgegate-mcp"], "env": { "EDGEGATE_API_KEY": "egk_live_...", "EDGEGATE_API_URL": "https://edgegateapi.frozo.ai" } } } }

Cursor (`~/.cursor/mcp.json`)

Same shape as Claude Code without the type field.

Claude Desktop (`~/Library/Application Support/Claude/claude_desktop_config.json` on macOS)

Same shape as Cursor.

Tools

See docs/tools.md for input schemas and examples.

EdgeGate exposes 61 MCP tools. This table is generated from src/server.ts — run npm run docs:tools after adding a tool.

Tool	Purpose
`edgegate_setup_workspace`	Confirm or list EdgeGate workspaces visible to the API key. Run this first in a fresh conversation to lock in which workspace_id the other tools should use.
`edgegate_create_pipeline`	Create a new EdgeGate regression pipeline. Define which model(s), which device(s), and which gates (e.g. inference_time_ms ≤ 10) the pipeline will enforce. For LLMs: set llm_compile_source on a model instead of artifact_id. EdgeGate will compile + link via AI Hub on first run; subsequent runs reuse the cached composite. ttft_ms + tps gates work; both are derived from per-component profile (prompt-role inference_time → TTFT; 1000/token-role inference_time → TPS). Each LLM gate run = 3 AI Hub profile jobs (one per component) ≈ 3× CV cost.
`edgegate_run_gate`	Trigger an EdgeGate run against a pipeline. Returns a run_id you can poll with edgegate_check_status.
`edgegate_check_status`	Get the current status of an EdgeGate run, including per-device metrics and which gates passed or failed.
`edgegate_get_report`	List recent EdgeGate runs in a workspace with status, duration, and trigger.
`edgegate_get_audit_report`	Get the signed audit report PDF URL for a completed EdgeGate run. Used for compliance records.
`edgegate_setup_github_action`	Generate the GitHub Actions workflow YAML + gh secret commands so every PR runs EdgeGate as a CI gate.
`edgegate_compare_runs`	Diff two EdgeGate runs — standard pipeline metrics/gates or Behavioral-Gate summary signals. Reports REGRESSION / IMPROVEMENT / NEUTRAL, or NO BASELINE / NOT COMPARABLE / INSUFFICIENT EVIDENCE when a reliable comparison cannot be made. Auto-selects a recent PASSED run from the same pipeline when baseline_run_id is omitted. Runs without a pipeline (including Behavioral-Gate runs) require an explicit baseline_run_id with matching eval-set and gate configuration.
`edgegate_export_run_report`	Download a human-readable markdown report for an EdgeGate run and save it to disk. Returns the absolute file path plus a preview of the first 30 lines. Optionally includes a run-vs-baseline diff section (include_diff=true).
`edgegate_import_huggingface_model`	Import a public Hugging Face model that contains a pre-built ONNX file. EdgeGate downloads the file and registers it as an Artifact. Returns the artifact_id you can pass directly to edgegate_create_pipeline. Polls until the import completes by default (poll_for_completion=true); set to false to return immediately with the job id.
`edgegate_predict_npu_coverage`	Predict which ONNX ops will run on the Qualcomm Hexagon NPU vs fall back to CPU, BEFORE spending any AI Hub credits. Returns a compute-weighted NPU coverage % (the latency-honest number), op-count coverage, risk band, per-op CPU fallbacks, and fix recommendations. Heuristic — the real device run remains authoritative.
`edgegate_list_promptpacks`	List all promptpacks in an EdgeGate workspace. Returns a markdown table with promptpack_id, version, case count, published status, and creation date. Use include_unpublished=false to hide draft packs.
`edgegate_create_promptpack`	Create a new promptpack in an EdgeGate workspace. A promptpack defines the test cases (prompts, expected outputs, per-case overrides) that regression pipelines evaluate. Requires admin role on the workspace. Packs are immutable after creation — bump the version to update.
`edgegate_publish_promptpack`	Publish a promptpack version in an EdgeGate workspace so it can be referenced in pipelines. Newly created packs start as unpublished — call this after edgegate_create_promptpack to complete the create → publish → use lifecycle. Requires admin role on the workspace. The operation is idempotent.
`edgegate_connect_huggingface`	Store a personal HuggingFace access token for this workspace so the import flow can read private / gated / Qualcomm-org repos (most qualcomm/, Intel/, and many Xenova/* repos 401 the anonymous endpoint). The token is validated against HF whoami before encryption and is never echoed in plaintext. If an integration already exists this tool rotates the token. Requires admin role.
`edgegate_get_huggingface_integration`	Show whether a personal HuggingFace token is connected to this workspace (and whether it is currently active or disabled). Does not return the token itself.
`edgegate_disconnect_huggingface`	Permanently delete the workspace's HuggingFace integration. Future HF imports fall back to anonymous access. Requires owner role.
`edgegate_connect_qaihub`	Store a Qualcomm AI Hub API token for this workspace so EdgeGate can submit compile + profile jobs on real Snapdragon devices. The token is encrypted at rest and is never returned in plaintext after the initial connect. If an integration already exists this tool transparently rotates the token. Requires admin role.
`edgegate_get_qaihub_integration`	Show whether a Qualcomm AI Hub token is connected to this workspace and whether it is currently active. Does not return the token itself.
`edgegate_disconnect_qaihub`	Permanently delete the workspace's Qualcomm AI Hub integration. Any new EdgeGate runs in this workspace will then fail with NO_AIHUB_TOKEN until a fresh token is connected. Requires owner role.
`edgegate_create_workspace`	Create a new EdgeGate workspace. The caller automatically becomes the owner. Subject to plan-tier workspace limits. After creation, connect Qualcomm AI Hub and define pipelines as usual.
`edgegate_list_api_keys`	List all API keys in this workspace (id, name, prefix...suffix, status, last_used). Plaintext is never returned. Requires owner role.
`edgegate_create_api_key`	Create a new API key for this workspace. The plaintext token is returned EXACTLY ONCE in the response — copy it to your CI secrets or local env immediately. Requires Pro tier or above. Requires owner role.
`edgegate_revoke_api_key`	Revoke an API key by id. The key is immediately rejected for all subsequent requests; the row is preserved (with revoked_at set) so the audit trail survives. Destructive. Requires owner role.
`edgegate_list_members`	List all members of this workspace with their email + role. Requires at least viewer role.
`edgegate_list_devices`	List every Qualcomm AI Hub device EdgeGate can target (Snapdragon phones, QRD/CRD reference platforms, IoT Dragonwing, automotive, XR). Returns a markdown table grouped by category. Use the `id` column verbatim when building a `create_pipeline` device matrix. No workspace_id needed — the catalog is global.
`edgegate_list_device_targets`	List the customer's OWN connected devices (Jetson, Snapdragon hosts, gateways) with live/offline status computed from each device's heartbeat (30s beat, 90s window). This is the workspace fleet connected via `edgegate-runner agent` — NOT the global AI Hub catalog (use edgegate_list_devices for that). If empty, the response includes copy-paste connect instructions for the customer.
`edgegate_run_device_benchmark`	Dispatch an ONNX benchmark to one of the customer's connected devices (Jetson, Snapdragon host, gateway) by name/id. The on-device agent picks it up within ~30s and reports latency/memory results. Multi-silicon: works for any vendor the customer has connected. Requires admin role.
`edgegate_invite_member`	Add an existing EdgeGate user to this workspace by email at the given role (owner / admin / viewer). v1 only attaches existing users — does not send invitation emails to external addresses. Requires admin role; only owners can add other owners.
`edgegate_change_member_role`	Change a member's role in this workspace. Cannot downgrade the last owner — promote another member to owner first. Requires owner role.
`edgegate_remove_member`	Remove a member from this workspace. The user loses access immediately; their pipelines and runs are preserved. Cannot remove the last owner. Destructive. Requires owner role.
`edgegate_setup_byo_storage`	Enterprise only. Zero-friction BYO storage setup — creates a pending grant with EdgeGate (returns the External ID) and returns the exact AWS CLI commands the agent should run to create the IAM role in the customer's AWS account. Pair with edgegate_attach_byo_role to finalize. Prefer this over edgegate_register_byo_bucket for new setups — the agent doesn't have to figure out the trust policy or guess at role names. Requires owner role. 402 from non-Enterprise workspaces; if a non-pending grant exists, instructs the agent to disconnect first.
`edgegate_attach_byo_role`	Phase 2 of edgegate_setup_byo_storage. Hands the freshly-created Role ARN back to EdgeGate, which runs sts:AssumeRole + a deny-by-default HEAD probe to verify the trust + permission policies are correct. Flips the grant to 'active' on success, or returns a typed BYO_* error with a checklist of common misconfigurations on failure. Re-callable with the same role_arn after fixing IAM.

| edgegate_register_byo_bucket | Enterprise only. **Use edgegate_setup_byo_

只显示了开头部分；其余内容请在仓库中阅读