Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
제작자가 쓴 설명(영어 원문)
Regression-test AI models on real Snapdragon and Jetson hardware, with signed evidence for CI.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
EDGEGATE_API_KEY, EDGEGATE_API_URL비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"edgegate": {
"command": "npx",
"args": [
"-y",
"edgegate-mcp"
],
"env": {
"EDGEGATE_API_KEY": "your-value",
"EDGEGATE_API_URL": "your-value"
}
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add edgegate --env EDGEGATE_API_KEY=your-value --env EDGEGATE_API_URL=your-value -- npx -y edgegate-mcp실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"edgegate": {
"command": "npx",
"args": [
"-y",
"edgegate-mcp"
],
"env": {
"EDGEGATE_API_KEY": "your-value",
"EDGEGATE_API_URL": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"edgegate": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"edgegate-mcp"
],
"env": {
"EDGEGATE_API_KEY": "your-value",
"EDGEGATE_API_URL": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
EDGEGATE_API_URL env이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검
레지스트리 이름이 GitHub 계정 frozo-ai 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
입력해야 할 값: EDGEGATE_API_KEY
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.19.0)은 3개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, OpenTakeoff
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Construction takeoff for AI agents: load plans, set scale, measure, count, export with provenance.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
MCP server for EdgeGate — set up edge-AI regression gates on Snapdragon devices directly from Claude Code, Cursor, or Claude Desktop.
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.
# 1. Generate an API key in the EdgeGate dashboard
npx --package=edgegate-mcp@0.21.2 edgegate-mcp-install
Restart your MCP client. Done.
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
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.
If you'd rather edit config files yourself, the server is a standard stdio MCP. Add this to your client's config:
~/.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/mcp.json)Same shape as Claude Code without the type field.
~/Library/Application Support/Claude/claude_desktop_config.json on macOS)Same shape as Cursor.
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_
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요