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Edgegate MCP

by frozo-aiversion 0.19.0i★ –iRuns on your computer
Registry nameio.github.frozo-ai/edgegate-mcp

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

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 on your computer
  • Transport: stdio
  • Category: Search & AI memory
  • You need to fill in: EDGEGATE_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • You fill in yourself: EDGEGATE_API_KEY, EDGEGATE_API_URL

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

  • npmNode.js installed (for the npx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

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

Run this in your terminal

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

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "edgegate": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "edgegate-mcp"
      ],
      "env": {
        "EDGEGATE_API_KEY": "your-value",
        "EDGEGATE_API_URL": "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 one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • EDGEGATE_API_KEY secret required 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.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

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 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account frozo-ai, 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 applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 5662 servers in this category
  • Published to the registry more recently than 28% of the other 5662 servers in this category
  • Basic checks equal to or better than 100% of the other 5662 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 Edgegate MCP 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: EDGEGATE_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.19.0) reached the registry 3 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

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

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday
Install

Codebase Memory

by DeusData

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

Runs on your computer★ 48,148MITUpdated 21 days ago
Install

Exa

by exa-labs

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

Runs in the cloud★ 5,213MITUpdated 2 months ago
Install

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.

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.

ToolPurpose
edgegate_setup_workspaceConfirm 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_pipelineCreate 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_gateTrigger an EdgeGate run against a pipeline. Returns a run_id you can poll with edgegate_check_status.
edgegate_check_statusGet the current status of an EdgeGate run, including per-device metrics and which gates passed or failed.
edgegate_get_reportList recent EdgeGate runs in a workspace with status, duration, and trigger.
edgegate_get_audit_reportGet the signed audit report PDF URL for a completed EdgeGate run. Used for compliance records.
edgegate_setup_github_actionGenerate the GitHub Actions workflow YAML + gh secret commands so every PR runs EdgeGate as a CI gate.
edgegate_compare_runsDiff 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_reportDownload 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_modelImport 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_coveragePredict 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_promptpacksList 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_promptpackCreate 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_promptpackPublish 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_huggingfaceStore 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_integrationShow 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_huggingfacePermanently delete the workspace's HuggingFace integration. Future HF imports fall back to anonymous access. Requires owner role.
edgegate_connect_qaihubStore 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_integrationShow 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_qaihubPermanently 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_workspaceCreate 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_keysList all API keys in this workspace (id, name, prefix...suffix, status, last_used). Plaintext is never returned. Requires owner role.
edgegate_create_api_keyCreate 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_keyRevoke 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_membersList all members of this workspace with their email + role. Requires at least viewer role.
edgegate_list_devicesList 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_targetsList 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_benchmarkDispatch 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_memberAdd 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_roleChange a member's role in this workspace. Cannot downgrade the last owner — promote another member to owner first. Requires owner role.
edgegate_remove_memberRemove 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_storageEnterprise 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_rolePhase 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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