Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Beschreibung vom Entwickler (englisches Original)
Regression-test AI models on real Snapdragon and Jetson hardware, with signed evidence for CI.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
EDGEGATE_API_KEY, EDGEGATE_API_URLAls geheim markierte Werte sind Schlüssel zu deinem Konto. Gib sie nie weiter und füge sie nie in einen Chat ein.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"edgegate": {
"command": "npx",
"args": [
"-y",
"edgegate-mcp"
],
"env": {
"EDGEGATE_API_KEY": "your-value",
"EDGEGATE_API_URL": "your-value"
}
}
}
}Führe das in deinem Terminal aus
claude mcp add edgegate --env EDGEGATE_API_KEY=your-value --env EDGEGATE_API_URL=your-value -- npx -y edgegate-mcpTrag die leeren Werte ein, bevor du ihn startest.
In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"edgegate": {
"command": "npx",
"args": [
"-y",
"edgegate-mcp"
],
"env": {
"EDGEGATE_API_KEY": "your-value",
"EDGEGATE_API_URL": "your-value"
}
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"edgegate": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"edgegate-mcp"
],
"env": {
"EDGEGATE_API_KEY": "your-value",
"EDGEGATE_API_URL": "your-value"
}
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
EDGEGATE_API_URL envDiese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto frozo-ai bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Das musst du eintragen: EDGEGATE_API_KEY
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.19.0) kam vor 3 Monaten in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Node.js auf deinem Computer.
In derselben Kategorie: Spec Driven Development, Remote Desktop Commander, Dialyne
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
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.
Real phone calls in France for AI agents: dial +33 numbers, menus, hold, AI disclosure.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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_
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