get_appFull details of one hosted application: what it does, how to use it, measured benchmark scores, source repository, and the URL a human can open to run it. Example — GET https://ainetcafe.com/t/get_app?slug=
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Application slug, from list_apps. |
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| slug | Yes | ||
| open_url | No |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "name": {
+ "type": "string"
+ },
+ "open_url": {
+ "type": "string"
+ },
+ "slug": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "slug",
+ "name"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only and idempotent behavior. The description adds value by disclosing the specific categories of information returned and providing a concrete example. It does not mention any side effects, which is consistent with the read-only 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?
The description is a single, information-dense sentence followed by a useful example. It avoids redundancy and front-loads the core purpose.
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?
With a single parameter, full schema coverage, and an output schema present, the description sufficiently covers what the tool does, how to call it, and what to expect. The example and parameter sourcing guidance make it complete for this simple tool.
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 already fully describes the slug parameter, including its source (list_apps). The description reinforces this with the example URL, but doesn't add new semantic meaning beyond the schema's description.
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 it returns full details of one hosted application, listing specific content (what it does, usage, benchmarks, repo, URL). This distinguishes it from siblings like list_apps (which lists apps) and build_app (which builds).
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 after list_apps, as the slug parameter is explicitly sourced from list_apps. It gives an example HTTP request showing how to invoke it. However, it doesn't explicitly state when not to use this tool vs checking jobs or building apps, though the context makes it fairly obvious.
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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