Fetch one Corduroy Labs update in full by its slug.
Args:
slug: the update's slug (e.g. "the-studio-machine-readable").
Obtain slugs from list_updates().
Returns:
A dict with slug, title, url, date_published, summary,
content_html, and optionally tags, location, image.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes the return format but does not explicitly state that the operation is read-only, nor does it mention side effects, authentication needs, or error cases. The lack of safety context reduces transparency.
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 concise, with two clear sections (Args, Returns) and no wasted text. The purpose is front-loaded in the first sentence. Every sentence adds value.
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 simplicity of the tool (one parameter, no output schema, no annotations), the description covers purpose, parameter semantics, and return format. Missing are details on error handling or authentication, but for a fetch tool this is largely sufficient.
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 has 0% description coverage for the slug parameter, so the description fully compensates by providing an example ('the-studio-machine-readable') and the source for obtaining slugs (list_updates()). This adds significant value 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 verb 'fetch' and the resource 'one Corduroy Labs update' with the identifier 'slug'. It distinctly separates from its sibling tool 'list_updates' by specifying 'in full' and the lookup mechanism.
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 explains when to use: to get a full update by slug. It also instructs how to obtain slugs from list_updates(), implying a sequence. However, it does not explicitly state when not to use, such as for creating or listing updates.
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.
The two tools have clearly distinct purposes: one fetches a single update by slug, the other lists updates. There is no overlap or ambiguity.
Both tools follow a consistent verb_noun pattern: get_update and list_updates. The naming is predictable and clear.
With only two tools, the server feels slightly minimal but appropriate for a read-only blog update service. It could benefit from one or two more tools for searching or filtering, but the count is not unreasonable.
The server covers the basic read operations (list and get by slug) for blog updates. Missing are search, filtering by tags or date, and pagination beyond a limit parameter. However, for a read-only surface, the coverage is adequate.