get_listingGet one published AI video tool by its slug.
Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {
"slug": "<slug>"
},
"name": "get_listing"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The listing slug. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=false and destructiveHint=false, so the safety profile is covered by structured data. The description adds one piece of real behavioral information beyond that — only *published* listings are retrievable — but says nothing about miss behavior (error versus empty result) or the returned shape.
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?
A single front-loaded sentence with zero filler; the scope (one, published, by slug) is packed into few words. It is arguably too terse rather than padded, which costs nothing on this dimension.
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 one-parameter, fully annotated, fully schema-described read tool with no output schema, this is close to sufficient — it states the resource, the key, and the published-only constraint. The remaining gap is what happens on a missing or unpublished slug, which an agent would need to call blindly to discover.
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%: the slug parameter already documents its type, pattern, and length bounds. The description only restates that the key is a slug plus the published-only qualifier, adding no format or canonicalization guidance beyond the schema's regex.
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 names a specific verb and resource ("get one published AI video tool") and constrains the lookup to a slug, so an agent knows this is a single-item fetch rather than a search or list. It does not explicitly contrast itself with nearby siblings like search_listings or list_courses, leaving that differentiation to the sibling names alone.
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?
Usage is implied: call this when you already have a slug and want the record for that one listing. There is no explicit when-not guidance (for example, use search_listings when you only have keywords or a name), and no mention of what to do if the slug is unknown.
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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