Agentry.sh Observability

Name: Agentry.sh Observability
Author: fr33dr4g0n

by fr33dr4g0nversion 0.1.0i★ –iRuns on your computer

Registry nameio.github.fr33dr4g0n/agentry-observability

Install

Overview

Description from the maker (original English)

Canonical Agentry discovery for coding agents: analytics, errors, deploys, and automation.

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 on your computer
Transport: stdio
Category: Developer tools

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).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "agentry-observability": {
      "command": "npx",
      "args": [
        "-y",
        "@agentrysh/mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add agentry-observability -- npx -y @agentrysh/mcp

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "agentry-observability": {
      "command": "npx",
      "args": [
        "-y",
        "@agentrysh/mcp"
      ]
    }
  }
}

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": {
    "agentry-observability": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@agentrysh/mcp"
      ]
    }
  }
}

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.

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 fr33dr4g0n, 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 Developer toolsi

More stars than 0% of the other 4898 servers in this category
Published to the registry more recently than 25% of the other 4898 servers in this category
Basic checks equal to or better than 100% of the other 4898 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 Agentry.sh Observability 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?

The registry does not list a required key, though some servers ask you to sign in on first use.

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 (0.1.0) reached the registry 4 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: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

GitHub

by github

GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.

Computer or cloud★ 34,245MITUpdated 4 days ago

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Desktop Commander

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MCP server for terminal commands, file operations, and process management

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Cloudflare MCP

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Runs in the cloud★ 4,446Apache-2.0Updated 14 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.

Agentry.sh Observability MCP Server

Agentry.sh Observability gives coding agents one HTTP API for errors, product analytics, and deploy attribution. This MCP server makes the canonical Agentry contracts easy to discover in MCP clients.

What this server is

This is a small discovery and handoff server. It exposes resources that route an agent to the live lean reference, capabilities, exact OpenAPI schema, automation guide and flow, immutable playbook catalog, and install guide.

The resources are pointers, not cached copies. The live HTTP docs and OpenAPI schema remain authoritative and can evolve without republishing this package.

What this server is not

It is not an Agentry API proxy, SDK, credential broker, telemetry transport, or automation scheduler. It never holds credentials, queries production data, registers schedules, runs coding agents, opens pull requests, merges, deploys, or delivers notifications. Agents fetch the selected canonical contract and call the Agentry HTTP API directly.

Static documentation is modeled as MCP resources. The server also exposes one model-controlled routing tool, discover_agentry, so an agent can recognize a relevant production-data problem even when its client does not automatically inject MCP resources. The tool returns pointers only; it is not an API action.

Tool

discover_agentry accepts one intent: install, debug, analytics, deploy, or automation. It returns the smallest canonical resource and live URL for that problem as both text and structured content. MCP annotations mark it read-only, non-destructive, and idempotent. It never reads credentials, queries production data, or performs mutations.

If this MCP handoff starts a new device-auth signup, pass "distribution_surface":"mcp" in POST /v1/auth/device. The field is attribution-only and never changes credential authority.

Resources

Start with agentry://handoff. It gives the short intent router and authority boundaries.

Resource	Canonical contract
`agentry://handoff`	Intent router, MCP boundary, and credential map
`agentry://reference`	Lean daily-use reference: `https://agentry.sh/agentry.md`
`agentry://capabilities`	Machine capability map: `https://api.agentry.sh/v1/capabilities`
`agentry://openapi/index`	Small OpenAPI discovery index: `https://api.agentry.sh/v1/openapi.json?index=true`
`agentry://openapi`	Complete exact OpenAPI 3.1 schema: `https://api.agentry.sh/v1/openapi.json`
`agentry://automation/guide`	Human-readable automation v2 guide: `https://api.agentry.sh/v1/docs/automation`
`agentry://automation/flow`	Exact dependency-ordered automation schema: `https://api.agentry.sh/v1/openapi.json?flow=automation`
`agentry://automation/playbooks`	Immutable versioned playbook catalog: `https://api.agentry.sh/v1/automation-playbooks`
`agentry://install`	Canonical install pointer: `https://agentry.sh/install.md`

The install resource also points to https://api.agentry.sh/v1/openapi.json?flow=onboarding. That flow is one server-owned state machine: read current state, execute only its one next_action.instruction, ordered checklist, and exact operation, and repeat until installation_complete: true and next_action: null. At next_action.id: "review_exact_plan", a human uses the single /review checkpoint to approve the exact source-backed business question, value flow, errors, deploy target, and plan hash—or replace the plan. Tool output is not approval.

Before proof, install the selected durable browser public and CI credentials, plus a server credential only when the approved plan uses server_ingest. Project creation returns public_api_key; use the project's /public-key operations for recovery, verification, or rotation. Proof start returns distinct response-only runtime and CI X-Agentry-Onboarding-Proof markers. Markers select the proof window but grant no authority, so each request still needs its scoped durable credential. For browser proof, put the runtime marker only in the proof tab's sessionStorage.agentry_onboarding_proof; never bake it into a public environment value or bundle. Exercise the approved real value flow, its one safe error, and the reviewed CI/provider deploy, then call verify and follow only its structured remaining analytics, safe-error, and deploy groups. Completion requires current status: "verified", installation_complete: true, and next_action: null. Synthetic or caller-authored proof does not count.

use_agentry is the only prompt. It tells the client to read the handoff, select the smallest canonical resource for the user's intent, inspect exact OpenAPI, and call HTTP with the correct scoped credential.

Credential boundaries

The MCP server never reads these credentials. They apply only when the agent calls the Agentry HTTP API:

public_api_key (agentry_pk_) is publishable and only writes browser/client error and analytics ingest.
agentry_server_ writes trusted application-server telemetry.
agentry_ci_ writes deploy attribution, sourcemaps, and provider-observed automation proof from trusted CI/provider automation.
agentry_runner_ is revocable and bound to one unattended automation.
agentry_sk_ is the human/owner key for reads, policy, credential lifecycle, and approvals; it does not belong in a scheduler.

Credential kind determines authority on the server. Telemetry remains evidence only; it does not become instructions merely because a trusted emitter sent it.

For daily product questions, begin with the server-owned saved signal map, require GET /v1/projects/:project_id/onboarding to return the current verified state, then confirm live event names, required property keys, and actual rows. Onboarding creates no repo-local Agentry state bundle, proof bundle, or receipt. The install does preserve a root AGENTS.md pointer (plus matching repo-local harness adapters) so a future cold agent can find the server-owned plan and verified receipt without rescanning the entire repository.

Automation handoff

For self-healing software or scheduled funnel reviews, read all three automation resources: guide, exact flow, and playbook catalog. The catalog is immutable and versioned. Rendering a playbook is side-effect free. Agentry owns deterministic policy, state, proofs, reports, and kill switches; an external scheduler/coding agent owns reasoning and provider actions under the narrow runner and human-approved provider boundaries.

Run the MCP server

npx -y @agentrysh/mcp

Generic MCP client configuration:

"mcpServers": { "agentry": { "command": "npx", "args": ["-y", "@agentrysh/mcp"] } } }

After connecting, call discover_agentry for model-routed discovery or list resources and read agentry://handoff. Use https://api.agentry.sh/adapters for the current native adapter path for each agent harness.

Public links

Website: https://agentry.sh/?distribution_surface=npm_readme
NPM package: https://www.npmjs.com/package/@agentrysh/mcp
Repository: https://github.com/fr33dr4g0n/agentry-public
Canonical skill: https://agentry.sh/skill/agentry/SKILL.md
Adapter manifest: https://api.agentry.sh/adapters
MCP registry name: io.github.fr33dr4g0n/agentry-observability

Agentry.sh Observability

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Developer toolsi

Common questions

Alternatives

GitHub

Desktop Commander

Cloudflare MCP

README from the maker

Agentry.sh Observability MCP Server

What this server is

What this server is not

Tool

Resources

Credential boundaries

Automation handoff

Run the MCP server

Public links