get_api_statusCheck whether a specific API is currently up, degraded, or down. Accepts an API name or slug (e.g. 'stripe', 'OpenAI', 'aws s3'). Returns current status, last measured latency, and when it was last checked. Use this to answer 'is X down right now?'.
| Name | Required | Description | Default |
|---|---|---|---|
| api | Yes | API name or slug, e.g. 'stripe', 'OpenAI', 'github'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey readOnly, idempotent, openWorld, and non-destructive behavior. The description adds valuable context about return contents: current status, last measured latency, and last-checked time. It does not contradict the annotations and is sufficiently transparent for a read-only status check.
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?
Three sentences with no wasted words: the first states the core behavior, the second covers input and output, and the third gives the intended use case. The most important information is front-loaded.
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?
For a simple one-parameter read-only tool with no output schema, the description is complete. It specifies accepted input, the possible statuses, the return fields, and a concrete usage scenario. An agent can invoke and interpret results without needing additional details.
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?
Schema coverage is 100%, and the schema already describes the 'api' parameter as an API name or slug with examples. The description repeats this and adds slightly more examples ('OpenAI', 'aws s3'), but it does not provide meaningful meaning beyond the schema.
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 checks whether a specific API is currently up, degraded, or down, with concrete examples like 'stripe' and 'OpenAI'. It distinguishes itself from plural/list-oriented siblings by emphasizing 'specific API' and 'currently', making its singular, real-time scope evident.
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 gives an explicit use case: 'Use this to answer "is X down right now?"' This tells the agent when to invoke the tool. It does not, however, state when not to use it or mention alternatives such as get_api_uptime or check_apis.
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.
Most tools have clear, distinct purposes: listing monitored APIs, checking individual status, checking multiple statuses at once, and retrieving uptime history. The only ambiguity is between check_apis and list_apis, as both return status information, though one targets specific APIs and the other provides a broader directory.
All tool names follow a consistent snake_case verb_noun pattern: check_apis, get_api_status, get_api_uptime, list_apis, list_recent_incidents. The verbs (check, get, list) are used predictably to distinguish action types, and nouns are descriptive.
Five tools is an ideal size for an API monitoring server. Each tool covers a distinct monitoring need without redundancy or bloat, and the scope is tightly focused on status and reliability queries.
The tool surface fully covers the core domain: discovering tracked APIs, checking current status individually or in bulk, retrieving historical uptime, and listing recent incidents. No essential monitoring operation appears to be missing for common agent workflows.