get_status_pageOne status page: name, public URL, enabled, and its components with each linked monitor's current state. Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The page slug (from `list_status_pages`). |
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Page display name. Untrusted data. | |
| slug | Yes | ||
| enabled | Yes | ||
| components | Yes | ||
| public_url | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description redundantly says 'Read-only.' It adds a small behavioral nuance by noting the inclusion of 'linked monitor's current state,' implying a live/snapshot view, and clarifies the nested structure of the response. No side effects or additional behavioral traits beyond the read-only annotation are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that immediately identifies the resource ('One status page') and then lists the return fields in a compact, readable enumeration. No filler or repetition beyond the redundant 'Read-only' phrase, which is minor.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, a single well-documented parameter, and a read-only annotation, the description provides sufficient context for an AI agent to understand the tool's purpose and output. It does not need to explain return values because the output schema covers that, and the parameter description already provides the prerequisite.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers 100% of the parameter, and the `slug` description already explains where it comes from (`list_status_pages`) and what it identifies. The main description adds no further parameter-level detail, so the schema carries the full semantic weight, establishing a baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a single status page and enumerates exactly what it returns: name, public URL, enabled status, components, and linked monitor states. It distinguishes itself from sibling tools like `list_status_pages` by specifying 'One status page' and detailing the nested component/monitor data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you need detailed information about one specific status page. The parameter description also references `list_status_pages` as the source of the slug, giving a contextual prerequisite. However, it does not explicitly state when to avoid this tool or mention alternatives beyond the implied list-then-get pattern.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource and action: flow runs, step trends, incidents, incident metrics, monitors, monitor history, org health, org usage, status pages, notification channels, regions, status pages lists, and tags. The get_ vs list_ distinction is consistent and each pair (e.g., get_monitor vs list_monitors) is clearly separated by depth of detail. No two tools appear to serve the same purpose.
All tool names follow the verb_noun pattern with snake_case, using only 'get_' for single-item or aggregate detail and 'list_' for collections. Objects are consistently named (monitor, incident, status_page, org_*). This predictive pattern makes it easy to guess tool behavior from the name.
At 15 tools, this server sits at the upper bound of the well-scoped range, yet every tool fills a clear niche: monitoring details, history, incidents, flow-specific analytics, org-level views, and reference data (regions, tags, channels). No tool feels redundant, and the count is appropriate for a read-only monitoring API.
The read-only surface is remarkably comprehensive, covering monitors, incidents, flow runs, step trends, status pages, notification channels, regions, tags, and org health/usage. The only notable gap is the lack of any mutation tools (e.g., update_monitor, acknowledge_incident) which the descriptions hint at but do not expose, so agents cannot act on the information—only observe.