ably_list_rulesList the integration rules (reactor rules: webhooks, AWS Lambda/SQS/Kinesis, Kafka, Pulsar, etc.) configured for an app. Control API: GET /apps/{app_id}/rules.
| Name | Required | Description | Default |
|---|---|---|---|
| app_id | Yes | App id (from ably_list_apps). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the safe read-only nature is covered. The description adds the endpoint and the specific rule types included, but does not disclose pagination behavior, output format, or any list-specific constraints. That is acceptable given the read-only annotation, but the description adds only modest behavioral context beyond the annotation.
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?
Two concise sentences: the first states the resource and examples, the second gives the exact endpoint. Every part earns its place, and 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 list operation with one clearly documented parameter, the description provides the endpoint, the resource scope, and examples of the rule types. It does not mention pagination or return shape, but no output schema exists and the listing nature is clear enough to call the tool correctly.
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 description coverage is 100% and the single app_id parameter is already documented as 'App id (from ably_list_apps).' The description repeats app_id in the endpoint URL but does not add further semantic detail beyond what the schema already provides. Baseline 3 is appropriate.
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?
States a specific verb and resource: list integration rules (reactor rules: webhooks, AWS Lambda/SQS/Kinesis, Kafka, Pulsar, etc.) configured for an app. It also gives the exact Control API endpoint, making the operation unambiguous and clearly distinct from sibling tools that list apps, channels, keys, or queues.
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?
Provides clear context: this lists integration rules for a specific app, and the app_id is supplied from ably_list_apps. It does not explicitly name alternative tools or state when not to use it, but the resource is unique among siblings, so an agent can infer appropriate usage without confusion.
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.
Every tool targets a distinct resource and action: account-level stats, app-level stats, channel details, history, presence, presence history, service time, and each control-plane listing (apps, keys, namespaces, queues, rules) are clearly separated. The only potentially confusing pair is account stats vs app stats, but their descriptions explicitly differentiate account-wide vs API-key-scoped usage.
All tools use a uniform 'ably_' prefix followed by a consistent verb_noun pattern: get_ for single resources, list_ for collections, publish_message for the write action, and whoami for token details. There are no mixed casing styles or inconsistent verb choices.
Fifteen tools is within the ideal range and each one covers a meaningful Ably capability, from control-plane inspection to channel data retrieval and publishing. The count feels justified given the breadth of Ably's API surface, with no redundant or filler tools.
The read side is well covered: stats, channel details, history, presence, and control-plane listings all have dedicated tools, and message publishing provides one write path. However, there are no create/update/delete operations for apps, keys, namespaces, queues, or rules, so lifecycle management is largely absent and an agent could not perform common administrative workflows.