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get_ping_instructions

Destructive

Get everything needed to make a monitor actually report: the ping URL, copy-paste check-in snippets, and the three MECHANISMS for reporting, returned together. Call this right after create_monitor. CHOOSE BY WHAT THE MONITORED THING IS — read reporting_options first and pick by that, rather than defaulting to the raw curl list: how_to — the manual protocol — is the UNIVERSAL path: it works in any agent, any language, any tool, with no prerequisite, so it is the default choice for any agent this applies to. Pair it with expect_every_s (the silence floor, set via update_monitor) so an agent that quietly stops reporting opens a detected incident instead of leaving its monitor reading healthy. If you ARE Claude Code specifically, hook_install is available as an OPTIONAL SHORTCUT, not a better tier: a one-time install that binds reporting to Claude Code's own hooks (UserPromptSubmit, Stop, StopFailure), automating how_to's exact same protocol so reporting becomes a property of your event loop instead of something you must remember — and it is the only mechanism that can send every state this product models, including blocked and note. hook_install is Claude Code specific: if you are a DIFFERENT AI agent — even one with its own hook or event system, Cursor, Windsurf, Codex, a custom framework — do NOT translate its steps into your own hooks; the event semantics differ and a translated install can pass its own verification while never reporting, so use how_to instead. If what you are monitoring is launched as a command instead — a cron job, a CI step, a script, or an agent started from a shell — use run_wrapper: wrap the command with lastping run and a separate process reports for you, so nothing has to be remembered; the tradeoff is that it reports the process's own lifecycle (start, success, fail, cancel) and has no way to send blocked or note. Whichever you choose, the underlying protocol is the same: the success ping at the END of the work, the fail URL if it failed, the start ping first for long or possibly-hung runs (this enables overrun / never-finished detection), and a step (curl_step) as each stage completes so a run that wedges mid-way is caught by name rather than only when its whole budget expires. Also read expectations_how_to: before you start work, use declare_run_expectations to say how THIS run should be judged when it closes — a one-time, unchangeable commitment that replaces the run grading itself. And discovery_how_to, which is about the OTHER jobs on this host or in this repo: how to find the scheduled work nobody is watching yet and propose it, rather than monitoring only the one thing you were asked about.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesMonitor UUID (from create_monitor or list_monitors).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description goes beyond annotations by explaining what is returned and how to interpret the returned reporting_options, how_to, expectations_how_to, and discovery_how_to. Annotations already indicate read/write and destructiveness, and the description does not contradict them; while it does not dwell on side effects, it gives substantial operational context about what an agent will receive and what to do with it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but front-loaded with the core purpose and then organized by decision path. Most sentences earn their place because they prevent mis-selection and clarify edge cases, though some repetition about hook_install could be tightened. It is dense but structured enough to remain usable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the burden of explaining return content and does so by naming the returned sections and how to choose among them. It also covers the underlying reporting protocol and links to related tools, so an agent has enough context to call the tool and act on its result without gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% coverage for the single id parameter with 'Monitor UUID (from create_monitor or list_monitors).' The description adds only workflow placement ('Call this right after create_monitor'), not new parameter-level semantics, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a precise verb and resource: 'Get everything needed to make a monitor actually report: the ping URL, copy-paste check-in snippets, and the three MECHANISMS for reporting, returned together.' This clearly differentiates it from sibling tools like get_monitor by focusing on reporting instructions rather than monitor state, and it anchors the workflow to create_monitor.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage guidance is exceptionally explicit: 'Call this right after create_monitor', then detailed selection rules ('CHOOSE BY WHAT THE MONITORED THING IS') with exact conditions for how_to, hook_install, and run_wrapper. It also gives negative guidance — telling non-Claude-Code agents not to translate hook_install — and references update_monitor and declare_run_expectations for follow-on behavior.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.7/5.0
Disambiguation4/5

Tools are organized by clear resource domains, and the long descriptions carefully separate similar reads. The main ambiguity is create_monitor's upsert behavior overlapping with update_monitor, and list_incidents vs list_open_incidents could be confused from names alone.

Naming Consistency4/5

Almost every tool uses verb_noun snake_case with a consistent CRUD vocabulary like create_, list_, get_, update_, and delete_. Minor deviations exist: get_alert_templates is really a list operation, and discover_monitors_reconcile is an awkward verb-object-verb construction.

Tool Count2/5

Thirty-six tools is a heavy surface for an agent to navigate, even for a full monitoring platform. Most tools are individually purposeful, but the count exceeds the range where an agent can quickly scan and select, and the set would be easier to handle if split across domains.

Completeness4/5

Coverage is strong: monitors, destinations, agents, status pages, API keys, routes, alert templates, incidents, run history, discovery, and Terraform export all have read and lifecycle operations. Minor gaps remain, such as no explicit single-incident view and no acknowledgment/resolution action beyond notes, but core workflows have no dead ends.